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of auditory stimuli and creation of stimulation sequences 5) Implementation of pilot studies in adults and infants 6) Programming analysis algorithms for FFR, MMN and statistical learning, based on spectral and
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, and Lifelines. Groups of patients with different prognoses will be identified through unsupervised clustering using algorithms such as K-means and NMF. To evaluate the tumor microenvironment, tools
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: Own the optimiser: design, implement, and tune heuristic/metaheuristic algorithms (e.g
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background in computer science or biomedical engineering, with a strong focus on programming using deep learning libraries and machine learning algorithms. Demonstrated experience in medical image processing
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the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1 point for required speciality. 2 points for required academic
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: The maximum score for the candidates who accomplish all the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1
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points) will be distributed as follows: 1 point for required speciality. 2 points for required academic training. 2 points for technic competences. 1 point for organisational competences. 3 points
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points) will be distributed as follows: 1 point for required speciality. 2 points for required academic training. 2 points for technic competences. 1 point for organisational competences. 3 points
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for the candidates who accomplish all the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1 point for required
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the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1 point for required speciality. 2 points for required academic