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- Tasks: Develop machine learning models to decode and engineer the immune system.
- Company: Join a cutting-edge biotech organisation focused on innovative biological solutions.
- Benefits: Enjoy flexible work options, collaborative culture, and opportunities for impactful research.
- Why this job: Be part of a mission-driven team tackling complex biological challenges with advanced technology.
- Qualifications: PhD or MSc in relevant fields; strong machine learning and data analysis skills required.
- Other info: Ideal for those passionate about immunology and computational biology.
The predicted salary is between 36000 - 60000 £ per year.
A cutting-edge biotech organization is seeking highly motivated Computational Scientists to support the mission of decoding and engineering the immune system. The role focuses on developing advanced machine learning and statistical models to analyze complex biological data, particularly immune repertoires and multimodal datasets.
About the Role
- Design and implement machine learning models—particularly language models, diffusion models, or graph neural networks—tailored to biomedical challenges.
- Build novel computational methods for interpreting biological sequences and structural data.
- Customize existing tools and develop new ones for integrative analysis and visualization of large-scale systems immunology data.
- Drive ML-based pipelines for diagnostic or therapeutic design.
- Benchmark computational methods and optimize performance across datasets.
- Lead or contribute to collaborative projects spanning academic, clinical, and industry domains.
Required Qualifications
- PhD (or MSc with equivalent experience) in Computational Biology, Bioinformatics, Computer Science, Statistics, Physics, or related quantitative discipline.
- Strong background in machine learning and statistical modeling, with a demonstrated ability to solve complex biological problems.
- Proven track record of scientific productivity (e.g., peer-reviewed publications).
- Hands-on experience in data handling, visualization, and biological data analysis.
- Proficient in Python, familiar with software development best practices.
- Practical experience with TensorFlow and/or PyTorch.
Preferred Qualifications
- 3+ years post-graduate experience in academia or biotech/pharma, applying ML/AI to biological datasets.
- Prior exposure to immunology, especially TCR/BCR repertoire analysis, or experience with protein design & or biologics.
- Deep expertise in at least one of the following areas:
- Language models for sequence analysis
- Diffusion models in molecular design
- Graph ML in biomedical networks
Computational Biology & Machine Learning Scientist employer: Skills Alliance
Contact Detail:
Skills Alliance Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computational Biology & Machine Learning Scientist
✨Tip Number 1
Network with professionals in the biotech and computational biology fields. Attend relevant conferences, webinars, or meetups to connect with people who work in similar roles. This can help you gain insights into the industry and potentially lead to referrals.
✨Tip Number 2
Showcase your projects and experience on platforms like GitHub or personal websites. Highlight any machine learning models you've developed, especially those related to biological data. This not only demonstrates your skills but also gives potential employers a tangible view of your work.
✨Tip Number 3
Stay updated on the latest advancements in machine learning and immunology. Follow relevant journals, blogs, and social media accounts to keep your knowledge current. Being well-informed will help you during interviews and discussions about the role.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and machine learning problems. Focus on Python, TensorFlow, and PyTorch, as these are crucial for the role. Familiarise yourself with common algorithms and their applications in biological contexts.
We think you need these skills to ace Computational Biology & Machine Learning Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in computational biology, machine learning, and any specific projects related to immunology or biologics. Use keywords from the job description to align your skills with what the company is looking for.
Craft a Strong Cover Letter: In your cover letter, express your passion for the role and the company's mission. Discuss your experience with machine learning models and how it relates to the challenges mentioned in the job description. Be specific about your contributions to past projects.
Showcase Your Publications: If you have peer-reviewed publications, mention them in your application. Highlight any that are particularly relevant to computational biology or machine learning, as this demonstrates your scientific productivity and expertise in the field.
Highlight Technical Skills: Clearly list your technical skills, especially in Python, TensorFlow, and PyTorch. If you have experience with GPU computing or specific machine learning techniques like language models or graph neural networks, make sure to include those details.
How to prepare for a job interview at Skills Alliance
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning models, particularly language models, diffusion models, and graph neural networks. Highlight specific projects where you've applied these techniques to solve biological problems.
✨Demonstrate Your Collaborative Spirit
Since the role involves working within a team, share examples of past collaborative projects. Emphasise your ability to work across academic, clinical, and industry domains, showcasing how you contributed to team success.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving skills in computational biology. Be ready to walk through your thought process on how you would approach a complex biological dataset or challenge using machine learning.
✨Familiarise Yourself with Current Trends
Stay updated on the latest advancements in computational biology and machine learning, especially in immunology and biologics. Being knowledgeable about recent publications or breakthroughs can demonstrate your passion and commitment to the field.
