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Field
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comparison of predictive algorithms—such as Random Forest and LSTM neural networks—for use in genetic material recommendation systems. Working as part of Camcore’s data science team, the selected candidate
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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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understanding of HLA donor search algorithms and other non-HLA factors, such as ABO, age and CMV status. Analyze donor searches and apply current donor selection algorithms and strategies. Responsible
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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programming (e.g., Python, MATLAB). Preferred Qualifications: Experience in resilience analysis for multi-agent systems. Familiarity with communication-constrained algorithm design. Prior work on communication
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data analysis programs. Work with lab members to design image analysis algorithms to fulfill the research goals. Publishes activities and findings. Analyzes complex microscopy and data problems and
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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
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microenvironment structures are associated with genomic features and clinical outcome. Danenberg E et al. Nat Genet. 2022 May;54(5):660-669. doi: 10.1038/s41588-022-01041-y Imaging mass cytometry and multiplatform
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, quality-diversity optimisation, constrained optimisation Genetic programming, code evolution, neuroevolution Fitness landscape analysis and visualisation Machine learning for algorithm performance