22 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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metagenomics assembly” funded by the Research Council of Finland in the research group of University Lecturer Leena Salmela. We develop models, algorithms and data structures for high throughput sequencing data
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well as newly developed computational methods. You will report your results in a scientific publication as a first author with the support of other members of the group. Skills, experience/qualifications
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a fully funded contract of up to 3 years. Position description The postdoctoral researcher position involves developing novel spatial and other statistical frameworks, combining biodiversity
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the research group of Professor Klaus Nordhausen in the project “Signal recovery in noisy spatial data”. The research group develops modern and efficient multivariate statistical methods tailored
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medication security are challenged by the high costs of development, manufacture and distribution, evaluation of preclinical safety and efficacy, and the dependence of drug manufacturing on international
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(FIMM) , University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our interdisciplinary team. Project overview This project aims to develop machine learning models
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development. The University provides support for internationally recruited employees with their transition to work and live in Finland. For more on the University of Helsinki as an employer, please see https
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environmental and health policy; measuring the societal costs of taxation; analyzing the influence of the public sector on firm birth, growth, and productivity; and taxation and development. Our research utilizes
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investigation and will develop an advanced computer modeling framework. By simulating processes at various scales, from the atomistic to continuum, we aim to reveal how temperature and saturation fluctuations
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at the Faculty of Medicine, University of Helsinki. The project will focus on using and extending deep learning-based approaches developed within the group to integrate bulk multi-omics cancer data. The Kuijjer