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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
close collaboration at all stages of a research project exploring applications of deep learning and artificial intelligence to dynamic portfolio optimization problems. This position offers the opportunity
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equivalent) in physics, computer science, mathematics, data science, computational science, and cognitive science Very good programming skills (e.g., in Python, C++, or comparable languages) Ideally, initial
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conferences and participation in project workshops in Wuppertal and Groningen (NL) Your Profile: Completed university degree (master`s or equivalent) in physics, computer science, mathematics, data science
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design and optimization of hydrogel coating systems. The innovation lies in establishing a closed-loop workflow specifically for interfacial mechanics, with multi-scale integration and physics-informed
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project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new generation of protein therapeutics
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characterization of the material. - Establish and optimize experimental protocols. - Check the performance of the instruments. - Ensure the training and supervision of users. 2. Experimental characterization
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, scientists with expertise in metabolic engineering, enzyme engineering, computational modelling, and process optimization will work together. By combining microbial strains with synthetic metabolism with
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research experience, preferably in programming languages, compilers, applied mathematics, and optimization techniques a strong background in compiler, code generation, and machine learning would be
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objectives. Duties of the position The successful candidate will be expected to complete their doctoral education and perform fundamental research, primarily within: Develop and optimize eco-friendly shell
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characterize polymer and dendrimer-based nanomaterials for targeted drug, and gene delivery. Develop and optimize linker chemistries and conjugation strategies for attaching therapeutics, targeting ligands, and