About this role
<b>Overview</b><br><div><p><span style="font-size: 10pt;"><a href="https://www.microsoft.com/en-us/research/lab/microsoft-research-ai-for-science" target="_blank" rel="noreferrer noopener"><span>Microsoft Research AI for Science</span></a><span> seeks a motivated Postdoctoral Researcher to design and lead experimental data-generation campaigns for the next Biomolecular Emulator (BioEmu) model. </span><a href="https://www.microsoft.com/en-us/research/lab/microsoft-research-ai-for-science" target="_blank" rel="noreferrer noopener"><span>Microsoft Research AI for Science</span></a><span> focuses on the development of machine learning and artificial intelligence methods for transforming molecular simulation and discovery of novel materials, drugs and chemical reactions. The BioEmu project aims to model the dynamics and function of proteins, how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in </span><a href="https://www.science.org/doi/10.1126/science.adv9817" target="_blank" rel="noreferrer noopener"><span>Science</span></a><span> (see our </span><a href="https://www.microsoft.com/en-us/research/blog/exploring-the-structural-changes-driving-protein-function-with-bioemu-1" target="_blank" rel="noreferrer noopener"><span>blog post</span></a><span> for links to our open-source software and other resources and this </span><a href="https://www.youtube.com/watch?v=LStKhWcL0VE" target="_blank" rel="noreferrer noopener"><span>explainer video</span></a><span>). </span><span> </span></span></p></div><div><p><span style="font-size: 10pt;"><span>This role is suited for researchers with either an experimental or computational background who are excited about connecting machine learning models with real-world biological measurements. They shall combine strong scientific judgement with clear communication, quantitative data interpretation and effective coordination across disciplines. The position does not include a dedicated wet-lab bench; experimental execution will primarily be carried out through external partners.</span> </span><span style="font-size: 10pt;"><span>This role emphasizes scientific ownership, cross-disciplinary collaboration, and scalable systems thinking, moving beyond one-off experiments or models to build reusable, high-impact data and modeling pipelines.</span><span> </span></span></p></div><div><p><strong><span style="font-size: 10pt;"><span>Why this role is exciting</span><span> </span></span></strong></p></div><div><p><span style="font-size: 10pt;"><span>You’ll be running very large-scale data generation campaigns to train next-generation AI methods that can make a meaningful impact on how biomolecular modeling is done and improve success rates in drug discovery. You provide your expertise on technical and design level, making decisions about and creating datasets that have crucial impact on our AI models. It’s an opportunity to bridge state‑of‑the‑art ML with meaningful biomedical impact in a highly collaborative research environment.</span><span> </span></span></p></div><br><br><b>Responsibilities</b><br><div><p><strong><span style="font-size: 10pt;"><span>1.Experimental campaign design, including areas such as</span></span></strong></p><ul><li><span style="font-size: 10pt;"><span>Design scalable campaigns for biomolecular interactions, conformational dynamics and related protein measurements. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Select systems, constructs, assays and controls based on scientific value, feasibility, diversity, throughput and cost. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Anticipate bottlenecks and define success criteria, contingency plans and follow-up experiments. </span><span> </span></span></li></ul></div><div><p><strong><span style="font-size: 10pt;"><span>2. CRO and external-partner leadership, including areas such as</span></span></strong></p></div><div><ul><li><span style="font-size: 10pt;"><span>Translate research goals into clear work packages, milestones and experimental requirements. </span> </span></li><li><span style="font-size: 10pt;"><span>Coordinate parallel programs with CROs and academic collaborators, review progress and guide corrective iterations. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Provide scientific direction on protein production, assay development and biophysical or structural characterization. </span><span> </span></span></li></ul></div><div><p><strong><span style="font-size: 10pt;"><span>3. Data quality and interpretation, including areas such as</span></span></strong></p></div><div><ul><li><span style="font-size: 10pt;"><span>Review raw and processed experimental outputs, including binding curves and kinetic measurements. </span> </span></li><li><span style="font-size: 10pt;"><span>Diagnose artifacts, failed fits and systematic assay problems using quantitative and biophysical reasoning. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Define reproducible QC criteria and scalable triage processes beyond manual review. </span><span> </span></span></li></ul></div><div><p><span style="font-size: 10pt;"><span><strong>4. Dataset construction and model integration, including areas such as</strong></span><span> </span></span></p></div><div><ul><li><span style="font-size: 10pt;"><span>Convert heterogeneous experimental outputs into traceable, model-ready datasets with appropriate metadata and provenance. </span> </span></li><li><span style="font-size: 10pt;"><span>Work with computational researchers to prioritize systems, evaluate model predictions and design informative follow-up experiments. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Use basic scripting and data-analysis tools to organize, inspect and summarize experimental datasets. </span><span> </span></span></li></ul></div><div><p><strong><span style="font-size: 10pt;"><span>5. Collaboration and research impact, including areas such as</span></span></strong></p><ul><li><span style="font-size: 10pt;"><span>Communicate experimental findings, limitations and risks to biological and computational collaborators. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Drive projects from ambiguous questions to usable datasets, scientific conclusions and publications. </span><span> </span></span></li><li><span style="font-size: 10pt;"><span>Contribute to the experimental data strategy for future BioEmu models.</span><span> </span></span></li></ul></div><br><br><b>Qualifications</b><br><div><p><span style="font-size: 10pt;"><strong><span>Required/Minimum Qualifications:</span><span><span> </span><span> </span></span><span> </span></strong></span></p></div><div><ul style="list-style-type: disc;"><li style="font-size: 10pt;"><div><span style="font-size: 10pt;">PhD in Biology, Biophysics, Biochemistry, Molecular Biology, Protein Science, Bioengineering, Computational Biology, Molecular Modelling, or a related field, with experience designing, conducting, or analyzing biomolecular experiments and/or computational studies.</span></div></li><li style="font-size: 10pt;"><div><span style="font-size: 10pt;"><span>Strong quantitative understanding of experimental measurements and their limitations. </span> </span></div></li><li style="font-size: 10pt;"><div><span style="font-size: 10pt;"><span>Ability to coordinate complex projects and communicate clearly across experimental and computational teams. </span> </span></div></li><li style="font-size: 10pt;"><div><span style="font-size: 10pt;"><span>Experience working with real-world biological, structural or biophysical datasets. </span> </span></div></li><li style="font-size: 10pt;"><div><span style="font-size: 10pt;"><span>Ability to independently own and deliver research projects. </span><span> </span></span></div></li></ul></div><div><p><span style="font-size: 10pt;"><strong><span>Preferred/Additional Qualifications:</span><span> </span></strong></span></p></div><div><ul><li style="font-size: 10pt;"><span style="font-size: 10pt;"><span><span>Experience managing CROs, </span><span>vendors</span><span> or distributed experimental collaborations.</span></span><span> </span></span></li><li style="font-size: 10pt;"><span style="font-size: 10pt;"><span><span>Expertise</span><span> in </span><span>protein-protein interactions, binder design, affinity </span><span>optimi</span><span>z</span><span>ation</span> <span>or</span><span> high-throughput assay </span><span>development. Familiar with </span><span>techniques</span><span> such as protein expression and purification, </span><span>binding assays (</span><span>SPR, BLI, ITC, cryo-EM</span><span>)</span><span>, </span><span>structural biology (</span><span>X-ray crystallography, NMR</span><span>)</span><span>, </span><span>Mass Spec (</span><span>HDX-MS</span><span>, Cross-link Mass </span><span>Spec</span><span>)</span><span>.</span></span><span> </span> </span></li><li style="font-size: 10pt;"><span style="font-size: 10pt;"><span><span>Practical Python or equivalent scripting skills for data analysis, </span><span>QC</span><span> and workflow automation.</span></span> </span></li><li style="font-size: 10pt;"><span style="font-size: 10pt;"><span><span>Experience </span><span>in </span><span>designing</span><span>, </span><span>curating</span><span> or standardi</span><span>z</span><span>ing datasets for machine-learning applications.</span></span><span> </span><span><span>Interest in model-guided experimental design, drug </span><span>discovery</span><span> or therapeutic applications.</span></span><span> </span><span> </span></span></li></ul></div> <br><br><p>The base pay for this internship is € 8,191.00 per month. Certain roles may be eligible for benefits and other compensation. </p><p></p> <p>Find additional benefits and pay information here:<br><a href="https://careers.microsoft.com/v2/global/en/corporate-pay/interns-corporate-pay.html">https://careers.microsoft.com/v2/global/en/corporate-pay/interns-corporate-pay.html</a></p><br>The base pay for this internship is £ 6,655.00 per month. Certain roles may be eligible for benefits and other compensation. <p></p> <p>Find additional benefits and pay information here:<br><a href="https://careers.microsoft.com/v2/global/en/corporate-pay/interns-corporate-pay.html">https://careers.microsoft.com/v2/global/en/corporate-pay/interns-corporate-pay.html</a></p><br><p>This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.</p><br><hr><br><p>Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about <a href="https://careers.microsoft.com/v2/global/en/accessibility.html"><b><u>requesting accommodations.</u></b></a></p>