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combined with in-situ validation data, including reflectance measurements and ecological surveys. This dataset will form the foundation for accurate training and validation of image classification models
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this position will be responsible for conducting research in the area of verification, testing and exploration of artificial intelligence models, active participation in lab meetings, scientific seminars and
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— an interdisciplinary field spanning information theory, stochastic thermodynamics, and quantum physics — and will explore several research directions: Thermodynamic computing: Developing physics-inspired models
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school or doctoral studies under the former law) in the same disciplines. Readiness to work with animal models (rats) Basic experience in molecular biology techniques is desirable Teamwork skills and
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work using animal models; demonstrates teamwork skills; possesses a high degree of independence and ability to plan work; has proficiency in English. The application should include: a cover letter
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scienceEducation LevelMaster Degree or equivalent Skills/Qualifications 1. Planning and conducting computational experiments on deep learning models for small molecules; 2. Implementing novel training techniques and
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theoretical knowledge of cell or tissue culture protocols, including gene expression analyses (qRT-PCR), Western blot, ELISA assays, and immunofluorescent staining. Authorization to conduct animal model
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in vivo PD models (α-Syn oligomer injection, behavioral testing, MRI) and in vitro studies (primary neuronal cultures, molecular and imaging techniques), we will dissect the role of P2X7R in: oxidative
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Institute of Fundamental Technological Research Polish Academy of Sciences | Poland | about 2 months ago
at the interface between cell biology and quantitative modeling Experience in wet-lab work, particularly in maintaining cell cultures, immunostaining, and microscopy imaging Ability to work in a team
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models and is carried out in collaboration with Prof. Graham Ogg, the scientific partner from the University of Oxford. The funding for this exciting project was obtained within the FIRST TEAM programme of