machine learning researcher

Raytheon

<div><p><b>Date Posted:</b></p>
2026-06-26<p><b>
Country:</b></p>
United Kingdom<p><b>
Location:</b></p>
Gloucester, South Gloucestershire<p><b>
Position Role Type:</b></p>
Onsite<p></p><p><b>
Data Scientist &amp; Machine Learning Researcher</b></p><p><b>
Gloucester, Manchester</b></p><p></p><div><div><p><b>
Benefits:</b></p></div><div><ul><li><p>
&pound;: Competitive salary</p></li></ul></div><div><ul><li><p>
Contributory Pension Scheme (up to 10.5% company contribution)</p></li></ul></div><div><ul><li><p>
6 times salary &lsquo;Life Assurance&rsquo; with pension</p></li></ul></div><div><ul><li><p>
25 days holiday (increasing with service) + statutory public holidays, plus opportunity to buy and sell up to 5 days (37hr)</p></li></ul></div><div><ul><li><p>
Company bonus scheme (discretionary)</p></li></ul></div><div><ul><li><p>
Flexible Benefits scheme with extensive salary sacrifice schemes, including Health Cashplan, Dental, and Cycle to Work amongst others</p></li></ul></div><div><ul><li><p>
Enhanced sick pay</p></li></ul></div><div><ul><li><p>
Enhanced family friendly policies including enhanced maternity, paternity &amp; shared parental leave</p></li></ul></div><p></p><div><p><b><br>
Work Culture</b></p></div><div><ul><li><p>
37hr working week, although hours may vary depending on role, job requirement or site-specific arrangements.</p></li></ul></div><div><ul><li><p>
Early 1.30pm finish Friday, start your weekend early!</p></li></ul></div><div><ul><li><p>
Remote, hybrid and site based working opportunities, dependant on your needs and the requirements of the role.</p></li></ul></div><div><ul><li><p>
Up to 5 paid days volunteering each year.</p></li></ul></div></div><div><div><ul><li><p>
Flexible working culture focused on output, with more formal flexible working arrangements on request (assessed subject to role) - please highlight any requests to the Talent Acquisition team.</p></li></ul></div></div><p></p><p><b>
About the role</b></p><p>
This role is within the Strategic Research Group (SRG). The SRG are a team of Data Science, Machine Learning and AI specialists who develop novel AI solutions to mission focussed problems. In this role you will be responsible for the technical development of AI/ML projects, working alongside more senior members of the team, supporting projects from initial idea scoping right through to final project delivery both in customer and internal domains.</p><p></p><p>
You will demonstrate novel thinking and propose new ideas for solving challenging problems. You will communicate these ideas effectively to senior technical leaders to feed into technical direction of your projects.</p><p></p><p><b>
Responsibilities</b></p><ul><li>
Develop, novel data science solutions, contributing significantly to machine learning projects as part of a team, with some guidance and direction from seniors.</li><li>
Bring experience in developing and delivering data-centric solutions.</li><li>
Undertake research and applied AI/ML tasks on both customer and internal research projects.</li><li>
Work with autonomy on assigned tasks within project groups.</li><li>
Generate ideas for new research directions.</li><li>
Deliver AI/ML/Data Science solutions to broad range of problems in defence.</li><li>
Work with more senior group members, customers and internal stakeholders to determine appropriate technical approaches and do the technical development required for delivery.</li><li>
Excellent communication skills, prepared to present and undertake practical demonstrations of work internally in the team and to senior stakeholders with adaptability to audiences of different levels of technical expertise.</li></ul><p></p><p><b>
Skills &amp; Experience</b><br>
Essential</p><ul><li>
BSc in Machine Learning, Data Science, Computer Science, Mathematics or related field.</li><li>
Experience coding in Python and associated ML packages (HuggingFace, TensorFlow, PyTorch).</li><li>
Have experience of delivering ML solutions to stakeholders.</li><li>
Demonstrate good understanding of AI/ML algorithms for different data types and tasks including Generative AI, NLP and computer vision, sufficient to be able to undertake research and development beyond existing literature, with some support and technical direction set by seniors.</li><li>
Experience of training and developing AI models including Large Language Models.</li><li>
Ability to produce high-quality scientific (academic style) writing for internal &amp; external publication.</li><li>
Problem solve with high levels of autonomy, with some input from technical leaders.</li><li>
Be a self-starter, being pro-active in finding solutions.</li></ul><p></p><p>
Desirable</p><ul><li>
PhD. or Masters degree highlighting experience of academic research.</li><li>
Experience using robust ML pipelines, appropriate version control and environment management (such as venvs or Docker)</li><li>
Working knowledge of Linux systems, using basic commandline functionality (e.g. AWS CLI, Docker CLI, Linux commands like ssh, ls, cd&hellip;)</li><li>
Experience working in Cloud, especially AWS but also equivalents such as GCP or Azure.</li><li>
Research publications in peer reviewed journals.</li><li>
Comfortable following agile processes for project delivery.</li><li>
Experience of delivering AI/ML projects in defence or government.</li></ul><p></p><p><i>
RTX adheres to the principles of equal employment. All qualified applications will be given careful consideration without regard to ethnicity,</i><i> </i><i>colour</i><i>, religion, gender, sexual orientation or identity, national origin, age, disability, protected veteran status or any other characteristic protected by law.</i></p><p></p><p>
#LI-BZ1</p><p></p><p><i>
RTX adheres to the principles of equal employment. All qualified applications will be given careful consideration without regard to ethnicity, color, religion, gender, sexual orientation or identity, national origin, age, disability, protected veteran status or any other characteristic protected by law.</i>
</p><p></p><p><b>Privacy Policy and Terms:</b></p></div><p></p>

Data Scientist & Machine Learning Researcher

Frontier Resourcing

<div><div><b>Principal Data Scientist &amp; Machine Learning Researcher
</b></div><div><p>Gloucester | London | Manchester
</p>
<p></p><p>Working with a leading National Security and Cyber organisation, you will help develop advanced AI and machine learning solutions that support critical national security missions.
</p>
<p>Operating within a mature Agile environment, you'll collaborate closely with customers, engineers, researchers and data scientists to solve complex real-world challenges using cutting-edge AI, machine learning and data science techniques.
</p>
<p>We have opportunities at all levels from Associate, Senior and Principal levels. Roles can be based in Gloucester, Manchester or London, with hybrid working available (typically 3 days per week on-site).
</p>
<ul><li>Due to the nature of work in National Infrastructure, candidates must hold an active eDV (enhanced DV) UK Security Clearance.
</li></ul>
<p></p><p><b>The Opportunity
</b></p><p>As a Senior Data Scientist &amp; Machine Learning Researcher, you will play a key role in the technical leadership, design and delivery of AI and machine learning projects from concept through to deployment.
</p>
<p>You'll work within a specialist team of Data Science, AI and Machine Learning experts, driving innovative research, developing advanced solutions and mentoring colleagues across a range of customer and internal R&amp;D programmes.
</p>
<p>This position offers the opportunity to contribute to genuinely challenging and impactful projects while helping shape future AI capabilities within the national security domain.
</p>
<p></p><p><b>Key Responsibilities
</b></p><ul><li>Design, develop and deliver complex machine learning and data science solutions with a high degree of autonomy.
</li><li>Lead technical investigations, research activities and AI/ML development across customer and internal projects.
</li><li>Identify innovative approaches to solving complex technical challenges and contribute to future research directions.
</li><li>Provide technical leadership within project teams and support successful project delivery.
</li><li>Collaborate with multidisciplinary teams, stakeholders and customers to define requirements and develop effective technical solutions.
</li><li>Mentor and support junior team members, helping to develop technical capability across the wider team.
</li><li>Communicate complex technical concepts clearly to both technical and non-technical audiences.
</li>
</ul><p></p><p><b>Essential Skills &amp; Experience
</b></p><ul><li>Degree (BSc or above) in Data Science, Machine Learning, Computer Science, Mathematics or a related discipline.
</li><li>Strong Python development experience, including machine learning frameworks such as Hugging Face, TensorFlow and PyTorch.
</li><li>Proven experience delivering machine learning solutions within commercial, research or government environments.
</li><li>Strong understanding of modern AI/ML techniques across areas such as Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP) and Computer Vision.
</li><li>Experience training, fine-tuning and evaluating machine learning and AI models.
</li><li>Ability to undertake technical research and apply findings to real-world challenges.
</li><li>Experience producing high-quality technical documentation, reports or research publications.
</li><li>Previous experience mentoring team members and providing technical leadership.
</li>
</ul><p></p><p><b>Desirable Skills &amp; Experience
</b></p><ul><li>MSc or PhD with a strong research background.
</li><li>Experience developing and maintaining robust ML pipelines and MLOps practices.
</li><li>Familiarity with version control, containerisation and environment management tools, including Git and Docker.
</li><li>Experience working within Linux environments and using command-line tooling.
</li><li>Experience deploying and scaling AI/ML solutions into production environments.
</li><li>Cloud platform experience, ideally AWS, although Azure or GCP experience is also valuable.
</li><li>Published research in peer-reviewed journals or conferences.
</li><li>Experience writing technical proposals, bids or research funding submissions.
</li>
</ul><p></p><p><b>What's on Offer
</b></p><ul><li>Opportunity to work on highly complex and meaningful national security challenges.
</li><li>Exposure to cutting-edge AI, Machine Learning and Data Science projects.
</li><li>Collaborative environment with experienced researchers, engineers and technical specialists.
</li><li>Clear opportunities for technical development, leadership and career progression.
</li><li>Hybrid working with locations in Gloucester, Manchester or London.
</li>
</ul><p></p><p><b>Benefits:
</b></p><ul><li>Competitive salary and excellent benefits
</li><li>eDV bonus on to of salary and annual bonus
</li><li>Company bonus scheme
</li><li>Contributory Pension Scheme (up to 10.5% company contribution)
</li><li>6 times salary &lsquo;Life Assurance&rsquo; with pension
</li><li>25 days holiday (increasing with service) + statutory public holidays, plus opportunity to buy and sell up to 5 days</li></ul></div></div>

Principal Data Scientist and Machine Learning Researcher (eDV)

Bayer

<p></p><div><p><b>At Bayer we&rsquo;re visionaries, driven to solve the world&rsquo;s toughest challenges and striving for a world where 'Health for all Hunger for none&rsquo; is no longer a dream, but a real possibility. We&rsquo;re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining &lsquo;impossible&rsquo;. There are so many reasons to join us. If you&rsquo;re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there&rsquo;s only one choice.</b></p><br>
<p></p>
<p><b>
Sr. Machine Learning Researcher, Domain-Aware Modeling &amp; Scientific Machine Learning</b></p><p></p><div><br>
<div>
<p>We are seeking a Sr. Machine Learning Researcher with strong expertise in the mathematical foundations of machine learning and scientific computing to develop next-generation domain-aware models for agriculture. This role sits at the intersection of applied mathematics, domain-aware modeling, and deep learning, with the goal of building models that respect and encode the underlying structure of biological and environmental systems. You will design principled, interpretable, and generalizable AI architectures that integrate scientific knowledge from genetics to crop physiology to environmental dynamics- into data-driven frameworks. Your work will directly enable transformative applications in genomic selection and genome editing target identification, accelerating the development of improved crop varieties worldwide.</p></div><div><br>
<p>
</p></div><div><p><b>YOUR TASKS AND RESPONSIBILITIES</b></p>
</div>
<div><p>The primary responsibilities of this role are:</p>
</div>
<div><ul><li><p>Scientific ML Model Development: Design, build, and validate domain-aware machine learning models (e.g., biology-informed, and hybrid mechanistic-statistical architectures) that incorporate prior scientific knowledge into learning algorithms for agricultural and genomic applications.</p>
</li>
</ul></div><div><ul><li><p>Mathematical Framework Design: Develop novel architectures and loss functions that embed biological constraints, conservation laws, symmetry properties, or known functional relationships into neural network training, ensuring physically and biologically consistent predictions.</p>
</li>
</ul></div><div><ul><li><p>Genomic Selection &amp; Editing Enablement: Architect models that leverage high-dimensional genomic, phenomic, and environmental data to predict complex trait outcomes, identify causal genetic variants, and prioritize genome editing targets with quantified uncertainty.</p>
</li>
</ul></div><div><ul><li><p>Uncertainty Quantification: Implement rigorous uncertainty quantification frameworks (Bayesian deep learning, ensemble methods, probabilistic surrogate models) to provide decision-makers with calibrated confidence estimates on model predictions.</p>
</li>
</ul></div><div><ul><li><p>Interdisciplinary Collaboration: Partner with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate domain expertise into model architecture decisions and validate model outputs against biological ground truth.</p>
</li>
</ul></div><div><ul><li><p>Scalable Deployment: Work with engineering and IT teams to transition research prototypes into production-grade models integrated within breeding and discovery pipelines, ensuring reproducibility, scalability, and maintainability.</p>
</li>
</ul></div><div><ul><li><p>Research Contribution: Contribute to publications in leading venues, participate in the internal scientific community, and stay at the frontier of scientific machine learning methodology.</p>
</li>
</ul></div><div><ul><li><p>Documentation &amp; Communication: Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organizational trust in AI-driven decision-making.</p></li></ul><br>
</div>
<p></p><div><p><b>WHO YOU ARE</b></p>
</div>
<div><p>Bayer seeks an incumbent who possesses the following:</p></div><div><br>
<p>
</p></div><div><p>Required:</p>
</div>
<div><ul><li><p>PhD in one of the following or closely related fields:</p>
</li>
</ul></div></div><div><div><ul><li><div><ul><li><p>Machine Learning / Deep Learning</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Applied Mathematics</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Computational Science &amp; Engineering</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Physics</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Chemical, Mechanical, or Biomedical Engineering</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Computer Science (with scientific computing or numerical methods focus)</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Statistics / Probabilistic Modeling</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Another related quantitative discipline with demonstrated depth in mathematical modeling</p>
</li>
</ul></div></li></ul></div><div><ul><li><p>Demonstrated research output (publications, thesis work, or applied projects) in scientific machine learning, numerical methods for differential equations, or data-driven modeling of physical/biological systems.</p>
</li>
</ul></div><div><ul><li><p>Proficiency in modern deep learning frameworks (PyTorch, JAX, or TensorFlow) and scientific computing libraries.</p>
</li>
</ul></div><div><ul><li><p>Experience formulating and solving problems involving high-dimensional, structured, or multi-modal data.</p>
</li>
</ul></div><div><ul><li><p>Strong communication skills and willingness to collaborate across disciplines.</p></li></ul><br>
</div>
<p></p><div><p>Preferred:</p>
</div>
<div><ul><li><p>5+ years post-PhD relevant experience</p>
</li>
</ul></div><div><ul><li><p>Demonstrated experience with one or more of the following domain-aware modeling paradigms:</p>
</li>
</ul></div><div><ul><li><div><ul><li><p>Physics-Informed Neural Networks (PINNs)</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs)</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs)</p>
</li>
</ul></div></li></ul></div><div><ul><li><div><ul><li><p>Operator learning methods (e.g., DeepONet, Fourier Neural Operator)</p>
</li>
</ul></div></li></ul></div></div><div><div><ul><li><div><ul><li><p>Hybrid mechanistic&ndash;data-driven models</p>
</li>
</ul></div></li></ul></div><div><ul><li><p>Experience with Bayesian inference, Gaussian processes, hierarchical models, or probabilistic programming.</p>
</li>
</ul></div><div><ul><li><p>Familiarity with nonlinear dynamics, dynamical systems theory, or systems biology modeling.</p>
</li>
</ul></div><div><ul><li><p>Background in surrogate modeling, model reduction, or multi-fidelity methods.</p>
</li>
</ul></div><div><ul><li><p>Exposure to genomics data structures (e.g., variant matrices, linkage disequilibrium, population genetics) or quantitative genetics (e.g., genomic BLUP, marker-effect models) - not required, but valued.</p>
</li>
</ul></div><div><ul><li><p>Experience deploying ML models into production environments (MLOps, containerization, cloud-based HPC).</p>
</li>
</ul></div><div><ul><li><p>Experience collaborating in interdisciplinary research teams spanning experimental and computational scientists.</p>
</li>
</ul></div><div><ul><li><p>Familiarity with ensemble methods, gradient-boosted models, kernel methods, or classical statistical learning as complementary tools.</p></li></ul><br>
</div>
<p></p><div><p>Employees can expect to be paid a salary of approximately $120k-170k. Additional compensation may include a bonus or incentive program (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.. This salary (or salary range) is merely an estimate and may vary based on an applicant&rsquo;s location, market data/ranges, an applicant&rsquo;s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.</p></div><div><br>
<p>
</p></div><div><p>This posting will be available for application until at least 6/26/26.</p></div><br>
</div><div>
<div></div><div><br>
<div><div><b>YOUR APPLICATION</b></div><br>
<div></div>
</div><br>
<br>
</div></div><div></div><div><div><div><div><p>Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.<br>
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.<br>
<br>
Bayer is an Equal Opportunity Employer/Disabled/Veterans<br>
<br>
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.</p></div><br>
<div></div>
</div><br>
<div><div>Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.</div>
<div></div>
</div><br>
<br>
<div></div><div><div>Bayer is an E-Verify Employer.</div></div><br>
<div></div>
<div>
</div><br>
<br>
<br>
<div></div><div><div><b>Location:</b></div> <div>United States : Residence Based : Residence Based || United States : Missouri : Creve Coeur
</div></div><br>
<div></div><div><div><b>Division:</b></div> <div>Crop Science</div>
</div><br>
<div></div><div><div><b>Reference Code:</b></div> <div>871164</div><br>
<div></div>
<div></div>
</div><br>
</div></div><div></div><div><div><br>
<br>
<div><div><b>Contact Us</b></div>
</div><br>
<br>
<div></div><div><div><b>Email:</b></div> <div>hrop_usa@bayer.com</div></div></div></div></div>

Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning

<div><p><b>Date Posted:</b></p><p><b>
Country:</b></p>
United Kingdom<p><b>
Location:</b></p>
Gloucester, South Gloucestershire<p><b>
Position Role Type:</b></p>
Unspecified<p></p><p><b>
Gloucester, London or Manchester</b></p><p></p><p><b>
Hybrid role: Must be prepared to work from a Raytheon or customer site depending on demand. Average of 3 days a week on-site.</b></p><p>
SC Required</p><p></p><p><b>
Must already hold or be able to gain an eDV clearance.</b></p><p></p><p><b>
Raytheon UK</b></p><p>
At Raytheon UK, we take immense pride in being a leader in defence and aerospace technology. As an employer, we are dedicated to fuelling innovation, nurturing talent, and fostering a culture of excellence.</p><p></p><p>
Joining our team means being part of an organisation that shapes the future of national security whilst investing in your growth and personal development. We provide a collaborative environment, abundant opportunities for professional development, and a profound sense of purpose in what we do. Together, we are not just advancing technology; we're building a community committed to safeguarding a safer and more connected world.</p><p></p><p><b>
About the role:
</b><br>
This role is within the Strategic Research Group (SRG). The SRG are a team of Data Science, Machine Learning and AI specialists who develop novel AI solutions to mission focussed problems. In this role you will work with high levels of autonomy and be responsible for the design, planning and technical leadership of multiple AI/ML projects. You will help lead and set technical and strategic direction for the group, support with customer and stakeholder engagement and deliver technical solutions on internal and external projects.</p><p></p><p><b>
Skills and Experience
</b></p><p><b>Requirements</b></p><ul><li><i>
BSc in Machine Learning, Data Science, Computer Science, Mathematics or related field.</i></li><li><i>
Multiple years experience of delivering novel ML solutions to customers and internal stakeholders.</i></li><li><i>
Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.</i></li><li><i>
Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.</i></li><li><i>
Experience of appropriate version control and environment management (such as venvs or Docker).</i></li><li><i>
Experience of writing technical project proposals.</i></li><li><i>
Experience of managing teams. This can be technical leadership and/or line management.</i></li><li><i>
Experience of leading teams following agile principles.</i></li><li><i>
Expert knowledge of AI/ML algorithms for different data types and tasks including Generative AI, NLP and computer vision, sufficient to be able to propose research beyond existing literature.</i></li><li><i>
Experience of training and developing AI models including Large Language Models.</i></li><li><i>
Ability to produce high-quality scientific writing for internal &amp; external stakeholders as well as academic publications.</i></li><li><i>
Ability to work with high levels of autonomy, with awareness of appropriate business policies and strategy to dictate your actions.</i></li></ul><p></p><p><b>
Desirable
</b></p><ul><li>PhD. or Masters degree highlighting experience of academic research.</li><li>
Experience of deploying AI models in a scalable way for external users.</li><li>
Experience developing supporting systems for AI deployments (e.g. backend databases, front end UIs)</li><li>
Working knowledge of Linux systems, using basic commandline functionality (e.g. AWS CLI, Docker CLI, Linux commands like ssh, ls, cd&hellip;)</li><li>
Experience working in Cloud, especially AWS but also equivalents such as GCP or Azure.</li><li>
Research publications in peer reviewed journals.</li><li>
Experience of delivering AI/ML projects in defence or government.</li><li>
Existing network of contacts across defence and government.</li></ul><p></p><p><b>
Responsibilities
</b></p><ul><li>Support the Head of Data Science &amp; Machine Learning using your experience to contribute to the group strategy and stakeholder engagement.</li><li>
Bring ideas for novel research that will drive competitive advantage in across our customer community.</li><li>
Work with autonomy to develop complex, novel data science solutions.</li><li>
Support in triaging and responding to opportunities, aligning to the group and wider business strategies.</li><li>
Design and deliver data-centric solutions while working collaboratively across disciplines.</li><li>
Technical delivery of research and applied AI/ML tasks on both customer and internal research projects.</li><li>
Provide technical leadership across multiple projects.</li><li>
Manage more junior team members across the SRG.</li><li>
Deliver AI/ML/Data Science solutions to broad range of problems in defence.</li><li>
Work with customers and internal stakeholders to determine appropriate technical approaches and do the technical development required for delivery.</li><li>
Excellent communication skills, prepared to present and undertake practical demonstrations of work internally in the team, to senior stakeholders and at conferences with adaptability to audiences of different levels of technical expertise.</li></ul><p></p><p><b>
Benefits and Work Culture</b></p><p><b>
Benefits</b></p><ul><li>
25 days holiday + statutory public holidays, plus opportunity to buy and sell up to 5 days (37hr)</li><li>
Contributory Pension Scheme (up to 10.5% company contribution)</li><li>
Company bonus scheme (discretionary).
</li><li>6 times salary &lsquo;Life Assurance&rsquo; with pension.</li><li>
Flexible Benefits scheme with extensive salary sacrifice schemes, including Health Cashplan, Dental, and Cycle to Work amongst others.</li><li>
Enhanced sick pay.</li><li>
Enhanced family friendly policies including enhanced maternity, paternity &amp; shared parental leave.</li><li>
Private Medical Insurance (dependant on grade)</li></ul><p></p><p><b>
Work Culture</b></p><ul><li>
37hr working week, although hours may vary depending on role, job requirement or site-specific arrangements.</li><li>
Early 1.30pm finish Friday, start your weekend early!</li><li>
Remote, hybrid and site based working opportunities, dependant on your needs and the requirements of the role.</li><li>
A grownup flexible working culture that is output, not time spent at desk, focussed. More formal flexible working arrangements can also be requested and assessed subject to the role. Please enquire or highlight any request to our Talent Acquisition team to explore the flexible working possibilities.</li><li>
Up to 5 paid days volunteering each year.</li></ul><p></p><p><b>
RTX
</b></p><p>Raytheon UK is a landed company and part of the wider RTX organisation. Headquartered in Arlington, Virginia, USA, but with over 180,000 employees globally across every continent, RTX provides advanced systems and services for commercial, military and government customers worldwide and comprises three industry-leading businesses &ndash; Collins Aerospace Systems, Pratt &amp; Whitney, and Raytheon.</p><p></p><p>
Supporting over 35,000 jobs across 13 UK sites, RTX is helping to drive prosperity. Each year our work contributes over &pound;2.7bn to the UK economy and offers a wealth of opportunities to 4,000 suppliers across England, Scotland, Wales and Northern Ireland. We&rsquo;re investing in all corners of the country, supporting 29,040 jobs in England, 3,040 in Northern Ireland, 1,900 in Scotland and 1,600 in Wales.</p><p></p><p>
#LI-VD1</p><p></p><p><i>
RTX adheres to the principles of equal employment. All qualified applications will be given careful consideration without regard to ethnicity, color, religion, gender, sexual orientation or identity, national origin, age, disability, protected veteran status or any other characteristic protected by law.</i>
</p><p></p><p><b>Privacy Policy and Terms:</b></p></div><p></p>

Principal Data Scientist & Machine Learning Researcher

TU Delft

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Postdoc Trustworthy Graph Machine Learning
Trustworthy Graph Machine Learning for Population Scale Networks
Job description
We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine learning for the analysis of population-scale networks. The position focuses on two pillars of trustworthiness: explainability and privacy-preserving learning.
This research direction connects closely to ongoing work by Dr. Megha Khosla on trustworthy graph machine learning, especially on the relationship between transparency and privacy in graph-based models. Population-scale network data are highly relational and often sensitive. Graph models for such data must therefore be accurate, interpretable and designed to reduce privacy risks.
A central aim of the position is to develop explainability methods for graph machine learning as a form of decision support. These methods should help researchers understand both model behaviour and the underlying data. They should explain why a model makes a prediction, what structural or demographic patterns the model captures, and when its decisions are reliable enough to support scientific interpretation or decision-making.
At the same time, explanations and learned graph representations can themselves reveal sensitive information. In population-scale networks, privacy risks may arise from rare individuals, sensitive attributes, neighbourhood structures etc.The postdoc will therefore investigate how explanations and graph learning methods can be made privacy-aware. This may include studying privacy risks in develoed models, designing explanations that avoid unnecessary disclosure, or developing new privacy-preserving graph learning techniques.
The project is funded by Macroscope, a Dutch national research infrastructure for studying social change, misinformation and trust at population scale.
We are especially interested in candidates with strong expertise in either explainable graph machine learning or privacy-preserving graph learning, together with a willingness to collaborate across the other area.
You will work with the task PI, Dr. Megha Khosla, and her team of PhD and Master&rsquo;s students. You will also collaborate with scientists from different fields across the Macroscope project, including computer science and computational social science. You will also get opportunities to expand your research network within the Computer Science departments and across the broader TU Delft research community.
Job requirements
PhD in computer science, AI, machine learning, data science, network science, computational social science, or a related field.
Expertise in either explainable graph machine learning or privacy-preserving graph learning.
Willingness to develop expertise across the other area.
Strong programming skills in Python, with experience using tools such as PyTorch, PyTorch Geometric, DGL, NetworkX, or scikit-learn.
Engineering skills and willingness/patience to work with large, noisy datasets, including preprocessing, pipeline development, and scalable evaluation.
Ability to work in an interdisciplinary team and communicate research clearly in English.
TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.
For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.
At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.
Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment &ndash; which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.
, Mathematics and Computer Science.
Conditions of employment
Duration of contract is 2 years Temporary
A job of 38 hours per week.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
An excellent pension scheme via the ABP.
The possibility to compile an individual employment package every year.
Discount with health insurers on supplemental packages.
Flexible working week.
Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
Plenty of opportunities for education, training and courses.
Partially paid parental leave
Attention for working healthy and energetically with the vitality program.
Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. .
Additional information
If you would like more information about this vacancy or the selection procedure, please contact Dr. Megha Khosla, via m.khosla@tudelft.nl.
Application procedure
Are you interested in this vacancy? Please apply no later than 26 July 2026 upload the following documents:
CV
Motivational letter
Research statement describing your own ambitions for the research line you would like to pursue, and explaining how this position would help you develop and achieve those ambitions.
You can address your application to Dr. Megha Khosla.
Please note:
You can apply online. We will not process applications sent by email and/or post.
As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
Please do not contact us for unsolicited services.
<br><div><div><div><div><div>Faculty/Department: Faculty of Electrical Engineering, Mathematics &amp; Computer Science
</div></div></div></div><div><div><div><div>Salary range: &euro;3546 - &euro;5538
</div></div></div></div><div><div><div><div>Hours per week: 38
</div></div></div></div><div><div><div><div>FTE: 1
</div></div></div></div><div><div><div><div>Submission is possible until: 26 Jul 2026
</div></div></div></div><div><div><div><div>ID job: 3547
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Postdoc Trustworthy Graph Machine Learning

Latent Labs

<div>
 The Opportunity
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 We are looking for a highly skilled machine learning researcher with significant experience in generative modeling. You will join an interdisciplinary team of machine learners, protein engineers and biologists, jointly working to change the way that we control biology and cure diseases. In your role you will architect novel generative models with the goal of designing new proteins that are functional in wet lab assays.
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 <strong>
  Who You Are
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 </strong>
 <ul>
  <li>
   You are a strong ML researcher with experience in generative modeling. You have led or worked on notable machine learning projects, as documented by your contributions to widely used open source libraries, significant product launches or high impact publications, e.g. at NeurIPS, ICML, ICLR or Nature venues. You have a proven track record of deep expertise in generative modeling.
  </li>
  <li>
   You are a skillful ML developer. You write ML code that is robust, tested and easy to maintain. You have experience using version control and code review systems. You are a fast prototyper and hacker who can also write beautiful production code. You have experience running training and inference on cloud hardware, parallelizing data and models across accelerators.
  </li>
  <li>
   You are a data engineer. You have experience building ML data pipelines for the training and evaluation of deep learning models. You are able to analyze the raw data, construct appropriate dataset splits and build pipelines that perform and scale.
  </li>
  <li>
   You are passionate about model performance. You have an intricate understanding of how ML libraries interplay with hardware and data and love to optimize deep learning models for training / inference speed. You have a deep knowledge of best principles and tricks in architecting deep learning models and use it to optimize how they perform on validation metrics.
  </li>
  <li>
   You are mission driven and curious. You are passionate about making a positive impact on the world, whether it's for patients, customers or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies. You are curious about problems, however small or big they appear. You thrive in a dynamic environment. You work well in a fast-paced setting where goals must be achieved efficiently and urgently.
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  What Sets You Apart
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 </strong>
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  <li>
   You have experience in computational biology or protein design. You have worked on ML-driven projects in biology.
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   You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields.
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  Your Responsibilities
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   Build machine learning models that work in the physical world [~90% of your time]:
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    Contribute to a careful curation of our training and evaluation data.
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    Propose and build ML evaluation metrics that align with real world success and company goals.
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    Quickly prototype generative models against our lead metrics and perform deep analyses of improvements.
   </li>
   <li>
    Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards.
   </li>
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    Contribute to the maintenance of our compute and ML development infrastructure.
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    In collaboration with the bio team, plan wet lab testing campaigns and carry out model inference against biological targets to enable their testing in the wet lab.
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    Quickly learn from wet lab results and feedback data to our models.
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  </ul>
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   Self development [~10% of your time]:
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    Stay on top of the latest developments in ML.
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    Gain a strong working understanding of protein and cell biology.
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    Participate in knowledge sharing, e.g. organize and present at our internal reading group.
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    Attend and present at conferences.
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   </li>
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  Apply
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 We offer strongly competitive compensation and benefits packages, including:
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   Private health insurance
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   Pension/401(K) contributions
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   Generous leave policies (including gender neutral parental leave)
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   Hybrid working
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   Travel opportunities and more
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  </li>
 </ul>
 We also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models.
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 We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills.
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