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Field
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competitive salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher education
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management, statistical analysis, interpretation of findings and preparation of study reports and publications (while participant recruitment and retention is outsourced). The successful candidate will also
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encourage applications from diverse candidates and are committed to creating an inclusive and supportive research environment. Job Responsibilities Data Analysis and Publication (70%) Manage and curate large
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and fabricating transparent and flexible microelectrode arrays (MEAs) based on conductive polymers for recording the electrical activity of enteric neuronal networks, as well as for performing cellular
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PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest in engaging and collaborating with
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, epigenomics, single-cell technologies, stem cell biology, muscle biology, aging, immunology, gene and cell therapy, computational analysis, or animal models are especially encouraged to apply. Strong motivation
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postdoctoral fellows, scientific and engineering staff, visiting scholars, as well as a large network of international collaborators. Networking opportunities extend to the group’s broad research program in low
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, Mathematics, Bioengineering or a related discipline PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest
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learning modelling expertise with experience in the analysis of challenging large-scale data sets. Experience with cellular imaging data or virology/immunology are a plus.Relevant skills include:•Experience
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large