computational chemist

Tap Growth ai

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  🌟 We're Hiring: Scientist Chemistry! 🌟
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 We are seeking to hire a
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  Scientist Chemistry
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 in
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  Waltham, MA.
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  📍 Location:
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 Waltham, United States
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  ⏰ Work Mode:
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 Work From Office
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  💼 Role:
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 Scientist Chemistry
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  Pay Scale:
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 Up to $93, 932.80 (Dependent on Experience)
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  What You'll Do
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 ∙Design and conduct MD simulations to explore structure, dynamics, and interactions
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 relevant to molecular interactions
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 ∙Apply coarse-grained MD techniques and contribute to methodological improvements where possible.
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 ∙Analyze and interpret simulation results; effectively communicate findings to multidisciplinary project teams
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 ∙Collaborate with experimental and therapeutic area colleagues to link modeling data to
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 biological and pharmacological outcomes
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 ∙Document methodologies, results, and best practices to enable knowledge transfer and reproducibility
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  What We're Looking For
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 ∙You have a PhD (or equivalent experience) in computational chemistry, biophysics, structural biology, or a related field.
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 ∙You have a proven track record with molecular dynamics simulation methodologies, including set-up, execution, and analysis
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 ∙Preferably, you bring hands-on experience with coarse-grained MD approaches (such as MARTINI or related frameworks)
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 ∙You are proficient in standard scientific programming/scripting (e.g., Python, Bash, or similar)
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 ∙You communicate scientific concepts clearly, both verbally and in writing
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 ∙Familiarity with other computational biophysics techniques (e.g., protein-protein docking, enhanced sampling)
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 Ready to make an impact? 🚀 Apply now and let's innovate together!
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Scientist Chemistry/ Computational Chemist

The Computational Chemistry List

<p></p><p><em>CCL</em> 25.08.19 PhD position in Computational Homogeneous Photocatalysis at KAUST</p><p>Conferences upcoming conferences Jobs positions open Consultants COnsulting Companies -Resumes- resumes (positions wanted) From: jobs at ccl.net (do not send your application there )<br>To: jobs at ccl.net<br>Date: Tue Aug :19:<br>Subject: <strong>25.08.19 PhD position in Computational Homogeneous Photocatalysis at KAUST</strong><br>Employer: KAUST - King Abdullah University of Science and TechnologyFrom: Prof. Luigi Cavallo, KAUST Catalysis Center, KAUST, Saudi ArabiaPosition offered: One PhD position in computational catalysis is available at the KAUST -King Abdullah University of Science and Technology-, Saudi Arabia (). The position is offered in the group of Prof. Luigi Cavallo (). The project will focus on developing a deeper mechanistic understanding of photocatalysis, mainly using static DFT and ab initio molecular dynamics of excited states of organic molecules and transition metal complexes. Within this scholarship the student will acquire the skills needed to build a successful career in independent research, by filling the gap between a student and a scientist. This implies mastering: i) the computational chemistry tools to carry out top-class research; ii) developing the needed chemistry knowledge to understand the problem at hand; iii) to communicate properly with experimental partners; iv) communicating research effectively in oral presentations and publication in journals; v) developing a critical approach to science; vi) write independent research proposals. Strong interaction with experimental groups is expected during the whole PhD. Students at KAUST are expected to graduate within 3-5 years.Further information on studying chemistry at KAUST is available at  :A KAUST scholarship will support the student during the whole stay at KAUST. Benefits include free fully furnished housing, free high standard medical and dental insurance. The KAUST is located on the red sea shore, close to Jeddah in the Kingdom of Saudi Arabia. Campus life at the KAUST is vibrant, with impressive recreational facilities that include excellent gym, climbing wall, martial arts classes, squash, racquet and tennis courts, basket and volley ball arena, beach volley, open air pools. Only applicants with the following qualifications will be considered.A recent Ms degree in Physics, Chemistry, or a related field.Good knowledge of physical chemistry.Proved experience in computational chemistry would be a plus.Excellent communication skills, both oral and written.Good judgment, independency at work, clear sense of purpose, and accountability.TOEFL for non-native English speakersUseful websites  kc2l.kaust.edu.sa/Pages/Home.aspx:  should include a CV and contact information of at least two references and should be submitted by email to Prof. Luigi Cavallo at luigi.cavallo at kaust.edu.sa<b>NOTE THAT E-MAIL ADDRESSES HAVE BEEN MODIFIED </b><br> were changed to  to fight spam. Before you send e-mail, youneed to change  example: change joe+ +to <br><em>Please let your prospective employer know that you learnedabout the job from the Computational Chemistry List Job Listing at</em> .If you are not interested in this particular position yourself, pass it tosomeone who might be   some day they may return the favor.</p><p>Page accessed 239 times since Tue Aug :24: GMT</p><img src="https://www.jobg8.com/Tracking.aspx?V5p2YvJYMMPyEtEJxQ7sZ23EC%2b1hEtFRb" width="0" height="0" />

25.08.19 PhD position in Computational Homogeneous Photocatalysis at KAUST

Aganitha AI

<div><div><div><h4 class="jobSectionHeader"><b>Join us and contribute to the discovery of medicines that will impact lives!</b></h4></div></div>
<div></div><div><div><div><div><div><div><h3 class="jobSectionHeader"><b>Hyderabad, India | Hybrid | Full-Time
</b></h3></div></div></div></div></div></div><div></div><div><div><div><div><div><div><h3 class="jobSectionHeader"><b>About Aganitha</b></h3></div></div><div><div><p>
High-throughput experimentation, computational &amp; data-driven modeling, and the advent of the Open Science era are fundamentally transforming research, discovery, and development across diverse industries. Aganitha is at the forefront of co-innovation with global clients, shaping next-generation R&amp;D (aganitha.ai). While our primary client base has been in global Biopharma, we are actively expanding our collaborations into consumer brands and, in the near future, the materials design industry.</p><p>
As a Scientist at Aganitha, you will be instrumental in leveraging cutting-edge advances in computational chemistry, materials science, soft matter physics, AI/ML, and high-performance computing in the Cloud. You will contribute to accelerating design and development across a spectrum of applications, including but not limited to: small molecule therapeutics, biologics, gene, cell &amp; RNA therapies within Biopharma; new product formulations for consumer brands; and novel materials for various industrial applications.</p><p>
You will collaborate closely with research leaders at our client organizations, identifying their needs and designing innovative solutions. Working with our internal technical and scientific teams, you will drive solutions from concept to launch and growth. You may also interact with external vendors to coordinate experimental validation of the <i>in silico</i> solutions developed at Aganitha.</p><p>
To excel in this role, you must possess a strong interest in engaging with customers to apply the latest scientific and technological advancements for R&amp;D acceleration, thereby contributing to Aganitha&rsquo;s growth.</p></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>
Key Responsibilities</b></h3></div></div><div><div><ul><li><b>
Design and develop AI/ML models</b> to solve complex problems, such as predicting molecular properties, material behaviors, or reaction outcomes.</li><li><b>
Curate, process, and analyze scientific datasets</b> from various sources, including literature and experimental data, ensuring data quality and readiness for model training.</li><li>
The candidate should be skilled in analyzing high-throughput omics datasets such as single-cell, spatial, and genomic data, and in computational modeling of biological systems, including network and pathway analysis. They should also have expertise in the in-silico design and optimization of therapeutic candidates such as antibodies, RNA molecules, and viral vectors. Proficiency in bioinformatics tools, biological databases, and the application of AI/ML approaches for biological data analysis and predictive modeling is essential.</li><li><b>
Develop intelligent featurization strategies</b> that accurately represent chemical structures, physical properties, or biological interactions, drawing upon your scientific understanding.</li><li><b>
Implement, train, and evaluate cutting-edge Machine Learning and Deep Learning algorithms</b> (e.g., CNNs, RNNs, LSTMs, Transformer architectures) to build robust predictive models.</li><li><b>
Rigorously validate models</b> against client-provided data and established benchmarks, ensuring their accuracy, generalizability, and utility.</li><li><b>
Translate complex technical and scientific findings</b> into clear, actionable insights for both technical and non-technical stakeholders.</li><li><b>
Collaborate effectively</b> with computational chemists, data scientists, and wet lab scientists to define project requirements, iterate on solutions, and ensure successful deployment.</li><li><b>
Stay abreast of the latest advancements</b> in AI/ML, computational chemistry, and relevant scientific domains, continuously seeking opportunities to apply new methodologies.</li></ul></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>
Qualifications</b></h3></div></div><div><div><ul><li><b>
PhD, post-doctoral research, or equivalent higher studies</b> in Computational Chemistry, Cheminformatics, Materials Science, Chemical Engineering, Biochemistry, Computational Biology, Bioinformatics, Computational Physics, Medicinal Chemistry, Applied Mathematics, Applied Statistics, Fluid Dynamics or a closely related scientific discipline.</li><li>
Familiarity with one or more of the following core domain skills: High-throughput docking and QSAR studies, Atomistic or Coarse-grained Molecular Dynamics (including Enhanced MD using PLUMED, Langevin Dynamics, Brownian Dynamics, and Dissipative Particle Dynamics), Periodic or non-periodic Quantum Chemical Calculations, Ab initio MD simulations and QM/MM simulations, Cheminformatics using RDKit, Mathematical Modeling, or Computational Fluid Dynamics.</li><li><b>
Strong foundational understanding</b> of the scientific principles underlying computational chemistry, materials science, or related fields (e.g., colloidal chemistry, polymer science, surfactant chemistry, soft condensed matter physics, etc.).</li><li><b>
Solid mathematical intuition</b> of Machine Learning algorithms, including Deep Learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, and Transformer architectures.</li><li><b>
Proficiency in modular, typed, and object-oriented Python programming.</b></li><li><b>
A high-level understanding of the ML/DL project lifecycle</b>, from data preparation and feature engineering through model development, training, evaluation, and deployment.</li><li><b>
Excellent problem-solving skills</b> and the ability to apply critical thinking to complex scientific and technical challenges.</li><li><b>
Strong verbal and written communication skills</b>, with the ability to effectively communicate technical concepts to diverse audiences.</li></ul></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>
Nice to Have</b></h3></div></div><div><div><ul><li>
Experience in effectively building and deploying ML solutions using popular frameworks such as PyTorch, TensorFlow, Keras, or scikit-learn.</li><li>
Proficiency in shell scripting.</li><li>
Proficiency in working with Linux-based operating systems (e.g., Ubuntu, Fedora, CentOS, Debian).</li></ul></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>
Big Plus</b></h3></div></div><div><div><ul><li>
Exposure to large language models (LLMs) such as ChatGPT, Claude, or Gemini, and practical experience in utilizing such tools for day-to-day work (e.g., writing research reports, understanding new concepts, generating code).</li><li>
Experience in optimal usage of High Performance Computing (HPC) resources.</li><li>
Familiarity with container-based usage of software and their dependencies (e.g., Docker).</li></ul></div></div></div></div></div></div></div><p></p>

Scientist (AI Modeling for Computational Sciences)

Lila Sciences

<div>
 <strong>
  About Lila
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 Lila Sciences is the world&rsquo;s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai
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 If this sounds like an environment you&rsquo;d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
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  Your Impact at Lila
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 Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You&rsquo;ll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimization&mdash;even when structures are uncertain or unavailable&mdash;ultimately accelerating DMTA cycles and improving candidate quality.
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  What You'll Be Building
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   Ligand-based QSAR modeling: Develop multi-task and transfer-learned models for potency, selectivity, and developability (e.g., solubility, permeability, clearance, CYP/hERG, safety liabilities) using graph/message-passing, and conformer-aware features. Handle activity cliffs, applicability domain, and calibration for robust decision-making.
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   Assay-driven hit triage and prioritization: Build models that learn from HTS, DEL, and follow-up assays; robust curve-fitting (4PL/5PL), plate/batch effect correction, dose&ndash;response QC, and time-split/scaffold-split evaluations to ensure prospective reliability.
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   Closed-loop DMTA and MPO: Create active learning and Bayesian optimization strategies to propose the next best analogs under multi-parameter objectives (potency, selectivity, exposure, safety, IP). Incorporate uncertainty, diversity, and experimental cost to maximize information gain per cycle.
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   Synthesis-aware design and retrosynthesis: Integrate template-based and template-free retrosynthesis with reaction prediction, condition and yield modeling, building-block availability, and cost/time/risk scoring. Make design suggestions that are directly makeable and prioritize routes compatible with internal/partner capabilities.
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   Generative and enumerative libraries: Build BRICS/RECAP/fragment-linking enumerations and property-conditioned generative models (diffusion/RL/flow) that respect synthetic constraints and matched molecular pair (MMP) rules for local SAR exploration and scaffold hopping.
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   SAR mining and explainability: Automate MMP analysis, local SAR maps, and substructure attributions to surface chemist-actionable insights; link assay deltas to specific modifications and highlight potential bioisosteres and de-risking moves.
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   Data foundations: Establish cheminformatics pipelines for standardization (tautomer/salt/charge), deduplication, structure normalization, and assay/ELN/LIMS ingestion; define ontologies and metadata for traceability and reproducibility.
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   Rigorous evaluation and deployment: Design leakage-safe splits (scaffold, temporal, series-aware), conformal prediction for calibrated decisions, and prospective tests. Ship APIs and tools that integrate with medchem workflows, procurement, and automated synthesis.
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   Cross-functional partnership: Work closely with medicinal chemists, DMPK, biology, and automation to translate TPPs into modeling objectives and to operationalize model recommendations in real make&ndash;test cycles. Collaborate with structure-based colleagues to fuse physics- and assay-derived signals where beneficial.
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  What You&rsquo;ll Need to Succeed
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   Strong proficiency in Python and modern ML (PyTorch/JAX/TF, scikit-learn, XGBoost/CatBoost), with experience training at scale and deploying end-to-end pipelines.
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   Deep experience in ligand-based modeling (QSAR/QSPR, multi-task learning, uncertainty and applicability domain, calibration) and ADMET prediction for medicinal chemistry.
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   Solid grasp of medicinal chemistry principles: SAR development, bioisosteres, property tuning (pKa/logD/PSA), selectivity design, and liability mitigation (CYP, hERG, reactivity, permeability, solubility).
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   Cheminformatics and data tooling: RDKit, Chemprop/DeepChem, conformer generation, fingerprints/descriptors, ELN/LIMS integration, and assay data processing/curve-fitting.
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   Retrosynthesis and synthesis planning: Familiarity with template-based/template-free methods, route scoring, reaction/yield/condition prediction, building block catalogs, and makeability constraints.
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   Active learning and design-of-experiments: Bayesian optimization, diversity sampling, and portfolio-aware selection under experimental and synthesis budgets.
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   Ability to design rigorous, leakage-controlled benchmarks and prospective validations; experience with scaffold/time splits and activity-cliff-aware evaluation.
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   Strong self-starter with excellent attention to detail and clear communication; able to collaborate tightly with chemists and biologists.
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   Demonstrated industry experience or academic achievement.
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  Bonus Points For
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   PhD in Chemoinformatics, Medicinal Chemistry, Computational Chemistry, Computer Science, or related field with a strong publication record in ML/drug discovery venues.
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   Experience building synthesis-aware generative models and integrating retrosynthesis into design loops; familiarity with tools like ASKCOS/AiZynth-style planners or equivalent.
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   Track record improving DMTA cycle time and MPO outcomes in live programs; integration with procurement and automated synthesis platforms.
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   Expertise with MMPA, activity-cliff handling, conformal prediction, and applicability-domain diagnostics in production.
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   Experience triaging HTS/DEL data, PAINS/aggregator/covalent liability filters, and off-target/polypharmacology prediction.
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   MLOps for cheminformatics: data versioning, experiment tracking, model serving/monitoring, and cloud/HPC scaling.
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  </li>
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  We&rsquo;re All In
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 Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
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  A Note to Agencies
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  Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science&rsquo;s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
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Machine Learning Scientist, Medicinal Chemistry & Lead Optimization

Lantern Pharma

<p>We are seeking talented and highly motivated <b>AI Drug Discovery Scientists</b> to develop innovative approaches supporting Lantern's internal drug development pipeline and external partnerships. You will work alongside cutting-edge teams in oncology drug development to accelerate the path from target to therapy.</p><p>The ideal candidate will bring innovative thinking to shift paradigms in drug discovery and development, with passion for matching right drugs to right indications faster and more cost-effectively.</p><p><b>RESPONSIBILITIES:</b></p><ul><li>Contribute to expanding, selecting, and appropriately applying datasets and AI/ML tools, integrating public and proprietary preclinical and clinical results to understand MOA and therapeutic potential</li><li>Analyze complex genomic contexts of cancer biological pathways to inform optimal MOA-indication matching strategies</li><li>Investigate relationships between MOA, therapeutic potential, and cancer genomics to identify biomarkers enhancing molecular selectivity</li><li>Evaluate chemical and structural features of molecules to support developmental modifications for NCEs with improved PK and efficacy</li><li>Uncover therapeutic potential of existing molecules using AI-driven analysis of drug response pathway interactions</li><li>Extract actionable insights from preclinical internal and external data sources to guide optimal combinatorial strategies</li><li>Apply AI methodologies for novel targeting of cancer drugs including ADCs and other targeted modalities</li><li>Build and maintain in silico molecule pipelines and infrastructure feeding AI/ML drug discovery bench projects</li><li>Collaborate with clinical and preclinical research teams to support proof-of-concept study designs</li><li>Contribute to scientific publications and grant applications</li></ul><p><b>REQUIRED QUALIFICATIONS:</b></p><ul><li>3+ years of experience working in AI-directed drug development environments</li><li>Ph.D. in Computational Biology, Bioinformatics, Computational Chemistry, Cancer Biology, or related field (Bachelor&rsquo;s or Master&rsquo;s degree with exceptional experience will also be considered)</li><li>Publication record in AI-based drug development</li><li>Demonstrated skills in matching ML-based in silico discovery of cancer drug response pathways and biomarkers to biological relevance</li><li>Working knowledge of deciphering MOA from complex genomic studies (CRISPR analysis, genetic screens) and utilizing AI for drug development strategy</li><li>Knowledge of genomic and cancer pathway data sources with experience utilizing diverse public biomedical databases for drug response interrogation</li><li>Experience in drug candidate prioritization within biopharmaceutical settings</li><li>Strong communication skills for preparing and presenting scientific data to diverse audiences</li><li>Proficiency in programming languages (Python, R, or similar)</li></ul><p><b>PREFERRED QUALIFICATIONS:</b></p><ul><li>Experience with grant writing</li><li>Team leadership or project management experience</li><li>Familiarity with remote/distributed team collaboration</li></ul><p>Job Type: Full-time</p><p>Work Location: In person</p>

AI Drug Discovery Scientist

kuwait shop

<p>Chemist needed for cosmetic production factory in Kuwait</p><p>1- A production chemist</p><p>Must have experience in the field of cosmetics and personal care products.</p><p>Good in English and computer skills.</p><p>Good knowledge of water desalination system.</p><p>2- Research and Development (R&amp;D) Chemist</p><p>Experience in cosmetics and personal care is required .</p><p>Good in English and computer skills .</p><p>Ability to develop and innovate new products .</p><p>3- QC Quality Control chemist</p><p>Bachelor's degree in Chemistry or a related field</p><p>Experience in the pharmaceutical or cosmetics analysis</p><p>Good knowledge of (HPLC, UV, IR instruments.)</p><p>Good knowledge of the GMP in pharmaceutical industries.</p><p>Good in English and computer skills .</p><p>Accommodation is available - Residence and annual renewal fees are covered</p><p>Please send your CV to</p><p>job@kuwaitshop1.com</p><p>Or what's app: 0096551781180</p><p>Salary range: $1000 to $1600</p><p>Job Type: Full-time</p><p>Job Type: Full-time</p><p>Pay: KD300.000 - KD500.000 per month</p>

chemical engineer

DataAnnotation

<div>
 We are looking for a Research Scientist (Chemistry) to join our team to train AI models. You will measure the progress of these AI chatbots, evaluate their logic, and solve problems to improve the quality of each model.
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 In this role you will need to hold an expert level understanding of chemistry. Other related fields include, but are not limited to: Research and Development Chemist, Formulation Scientist, Development Chemist, Analytical Chemist, Chemical Engineer, Medicinal Chemist, Biochemist, Process Development Chemist.
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  Benefits
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   This is a full-time or part-time REMOTE position
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   You&rsquo;ll be able to choose which projects you want to work on
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   You can work on your own schedule
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   Projects are paid hourly starting at $40+ USD per hour, with bonuses on high-quality and high-volume work
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  Responsibilities
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   Give AI chatbots complex chemistry questions and evaluate their outputs
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   Evaluate the quality produced by AI models for correctness and performance
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  Qualifications
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   Fluency in English (native or bilingual level)
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   Detail-oriented
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   Solid knowledge of general, organic, and physical chemistry concepts
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   A current, in progress, or completed Masters and/or PhD is preferred but not required
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  Notes
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 Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in the United States will be considered for this role. This is an independent contract position.
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 #chem
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Research Scientist (Chemistry)

Search 248+ Computational chemist jobs near you this October. New roles added regularly across leading industries. Apply today.