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processes. • Adoption of digital technologies, sustainability, and circular business models. KEY RESPONSIBILITIES Lead and conduct innovative, high-impact research within one of FEC’s core research areas
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Predoctoral researcher at the Targeted Therapeutics & Nanodevices Research Group (Project BRAINZYME)
physiological conditions. • Examine their performance in 2D and 3D systems, including chemical and genetic disease models, multi-cultures and organ-on-a-chip devices. • Investigate the biodistribution as
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science, or closely related previous sufficient experience/education in the following areas: ecosystem services, risk assessment, ecological/environmental data analysis and modelling, soil biology Informal
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, astrophysics, or a related discipline completed by the time of appointment, Strong interest in solar physics, space weather, or plasma modeling, Programming experience (e.g., Python or equivalent), Some
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Details Title Pre-Doctoral Fellowship (Profs. Laibson/Choi/Beshears) School Faculty of Arts and Sciences Department/Area Economics Position Description Economics professors David Laibson (Harvard
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in the areas of long-term care, whole-person care models, and caregiver support. Minimum Requirements: Eligible candidates should have a doctoral degree (Ph.D. or equivalent) in any of the following
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the matching of models to real-world targets, and that a better understanding of this class of methods will provide insights for planning and evaluating translational research. This project will be primarily
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technologies including thermal energy storage (TES) and hydrogen technologies integrating with concentrating solar power (CSP). Responsibilities under this position include leading research work on modeling
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Subject description This position will be linked to the project: Insect
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will generate a wide range of contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models