Deep Learning for Clinical Trial Data
Budget: €1,500 – €3,000 EUR
Medical Data Scientist – Evidence-Based AI for Longevity
About the project:
LAX is a longevity-focused health platform that uses AI to deliver evidence-based recommendations across three pillars: Lifestyle, Biomarkers, and Biohacking.
In this initial phase, the goal is to build the scientific foundation of the AI system — a model that can evaluate, structure, and prioritize medical evidence (studies, guidelines, and meta-analyses) to generate transparent and reliable insights for preventive and longevity medicine.
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Key Responsibilities
• Develop and implement a scientific evidence-evaluation framework for the LAX AI
• Integrate and process data from medical literature and clinical trials (e.g., PubMed, OpenAlex, ClinicalTrials.gov)
• Apply natural language processing (NLP) and machine learning to extract and classify key findings from medical studies
• Build a transparent evidence-weighting model (e.g., Meta-analyses > RCTs > Observational studies)
• Collaborate with the LAX medical and research teams to define evidence criteria and data pipelines
• Prepare data structures and algorithms that can later be scaled into deep learning models for personalization
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Required Skills & Experience
• Degree in Health Data Science, Medical Informatics, Biostatistics, or related field
• Proven experience with medical or clinical research data
• Proficiency in Python (Pandas, NumPy, Scikit-Learn, or PyTorch/TensorFlow)
• Experience in data preprocessing, NLP, and evidence extraction
• Understanding of clinical trial design and medical evidence hierarchies
• Strong analytical and critical-thinking skills with scientific rigor
⸻
Nice to Have
• Experience building knowledge graphs or evidence ontologies
• Background in longevity, preventive medicine, or digital health
• Familiarity with AI transparency and explainability in healthcare
⸻
Project Details
• Remote position (part-time, approx. 2 h/day)
• Duration: 2 months (with potential for extension)
• Compensation: 62 CHF/hour (approx. 5000 CHF total)
• Start date: As soon as possible
About the project:
LAX is a longevity-focused health platform that uses AI to deliver evidence-based recommendations across three pillars: Lifestyle, Biomarkers, and Biohacking.
In this initial phase, the goal is to build the scientific foundation of the AI system — a model that can evaluate, structure, and prioritize medical evidence (studies, guidelines, and meta-analyses) to generate transparent and reliable insights for preventive and longevity medicine.
⸻
Key Responsibilities
• Develop and implement a scientific evidence-evaluation framework for the LAX AI
• Integrate and process data from medical literature and clinical trials (e.g., PubMed, OpenAlex, ClinicalTrials.gov)
• Apply natural language processing (NLP) and machine learning to extract and classify key findings from medical studies
• Build a transparent evidence-weighting model (e.g., Meta-analyses > RCTs > Observational studies)
• Collaborate with the LAX medical and research teams to define evidence criteria and data pipelines
• Prepare data structures and algorithms that can later be scaled into deep learning models for personalization
⸻
Required Skills & Experience
• Degree in Health Data Science, Medical Informatics, Biostatistics, or related field
• Proven experience with medical or clinical research data
• Proficiency in Python (Pandas, NumPy, Scikit-Learn, or PyTorch/TensorFlow)
• Experience in data preprocessing, NLP, and evidence extraction
• Understanding of clinical trial design and medical evidence hierarchies
• Strong analytical and critical-thinking skills with scientific rigor
⸻
Nice to Have
• Experience building knowledge graphs or evidence ontologies
• Background in longevity, preventive medicine, or digital health
• Familiarity with AI transparency and explainability in healthcare
⸻
Project Details
• Remote position (part-time, approx. 2 h/day)
• Duration: 2 months (with potential for extension)
• Compensation: 62 CHF/hour (approx. 5000 CHF total)
• Start date: As soon as possible