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- Tasks: Apply machine learning to predict patient responses in triple negative breast cancer.
- Company: Leading UK research university with a focus on biomedical innovation.
- Benefits: Competitive salary, training opportunities in Germany, and collaboration with international researchers.
- Why this job: Make a real difference in cancer research while advancing your career in a dynamic environment.
- Qualifications: PhD in a relevant field and strong analytical skills.
The predicted salary is between 38784 - 42254 £ per year.
A UK-based research university is seeking a Research Fellow to work on a project applying machine learning to predict patient response in triple negative breast cancer. This role requires a PhD in a relevant field and offers a salary between £38,784 and £42,254 per annum, with additional training opportunities in Germany. The successful candidate will collaborate with international researchers and be involved in the university’s biomedical community.
Postdoc: Machine Learning for Cancer Response (TNBC) employer: Abdn
Contact Detail:
Abdn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Postdoc: Machine Learning for Cancer Response (TNBC)
✨Tip Number 1
Network like a pro! Reach out to researchers in the field of machine learning and cancer response. Attend conferences or webinars where you can meet potential collaborators and get your name out there.
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your previous work in machine learning, especially any projects related to healthcare or cancer research. This will give you an edge when discussing your experience.
✨Tip Number 3
Prepare for interviews by brushing up on relevant topics. Be ready to discuss how your PhD work relates to predicting patient responses in triple negative breast cancer. We want to see your passion and expertise shine through!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Postdoc: Machine Learning for Cancer Response (TNBC)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in machine learning and cancer research. We want to see how your skills align with the project, so don’t hold back on showcasing your PhD work!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about applying machine learning to cancer response. We love seeing enthusiasm and a clear connection to our mission.
Highlight Collaborative Experience: Since this role involves working with international researchers, be sure to mention any past collaborative projects. We value teamwork, so let us know how you’ve successfully worked with others in your field.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your materials and ensures you’re considered for this exciting opportunity.
How to prepare for a job interview at Abdn
✨Know Your Machine Learning Inside Out
Make sure you brush up on your machine learning concepts, especially those relevant to cancer research. Be prepared to discuss specific algorithms and their applications in predicting patient responses, particularly in triple negative breast cancer.
✨Showcase Your Research Experience
Highlight your PhD work and any relevant projects you've been involved in. Be ready to explain how your previous research aligns with the goals of the project and how it can contribute to the university's biomedical community.
✨Prepare for Collaborative Questions
Since this role involves working with international researchers, think about your past experiences in collaborative settings. Be prepared to share examples of how you’ve successfully worked in teams and navigated different perspectives.
✨Ask Insightful Questions
Prepare thoughtful questions about the project and the team dynamics. This shows your genuine interest in the role and helps you assess if the environment is a good fit for you. Consider asking about the training opportunities in Germany and how they integrate with the research.
