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will have the following required qualifications: an MSc degree with specialisation in ecology, evolution, biological oceanography or a closely related discipline; experience with genetic methods (DNA
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to sustainable food production; an interest in genomic prediction and statistical and quantitative genetics; a desire to further develop your research skills and apply them into practice by developing solutions
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corporate and NGO network for impactful research. We are seeking applicants with a solid quantitative background and an demonstrated interest in empirical research. Candidates with a MSc, MPhil, or Research
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Website https://www.academictransfer.com/en/jobs/350125/phd-candidate-in-causal-learnin… Requirements Specific Requirements You are/ You have: a MSc degree in statistics, machine learning, data science or a
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statistical programming languages, such as R or Python; (ideally) has gained some experience in writing scientific publications. Our offer A position for one year, with an extension to a total of four years
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English (C1 level) and has excellent communication skills; is well-versed in statistical programming languages, such as R or Python; (ideally) has gained some experience in writing scientific publications
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models
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. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective