At a Glance
- Tasks: Integrate machine learning with physics-based simulations for drug discovery.
- Company: Collaborative partnership between Genesis Therapeutics and Newcastle University.
- Benefits: 100% home fees covered, tax-free living allowance, and additional project funding.
- Other info: Exciting opportunity to work on innovative projects over 4 years.
- Why this job: Transform drug discovery with cutting-edge tech and make a real impact in healthcare.
- Qualifications: Strong background in computational physics or machine learning.
The predicted salary is between 19237 - 19237 £ per year.
Award Summary: 100% home fees covered, and a minimum tax‑free annual living allowance of £19,237 (2024/25 UKRI rate). An additional allowance will be provided to contribute towards consumables, equipment, and travel related to the project.
Overview: Machine learning aims to transform the drug discovery landscape through the prediction of potential new therapeutics with unprecedented speed and accuracy. Yet this approach works best when combined with physics‑based modelling, in which the interactions between potential drugs and their target receptor are explicitly modelled at the atomic scale. In our previous work we have shown that classical (force field) models with novel functional forms have the flexibility to model interactions in the condensed phase with higher accuracy than traditional (Lennard‑Jones based) models.
Responsibilities: The student will further incorporate high‑level quantum mechanical data and machine learning to improve the scope of these models such that they accurately model interactions between proteins, small organic molecules and water, at a fraction of the cost of quantum mechanics. They will integrate the workflows into drug discovery pipelines at Newcastle University and/or at the industry partner, and thereby showcase the potential of physics‑based modelling for the design of future therapies.
Eligibility Criteria: Please see the MoSMed website for further details regarding academic eligibility. The studentship covers fees at the Home rate (UK and EU applicants with pre‑settled/settled status and meet the residency criteria). International applicants are welcome to apply but will be required to cover the difference between Home and International fees. International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme.
Number Of Awards: 1
Start Date: 16th September 2024
Award Duration: 4 years
Application Closing Date: 21st May 2024
Sponsor: Genesis Therapeutics/Newcastle University
Supervisors: Dr Daniel Cole and Prof Martin Noble (Newcastle) and Dr Simon Boothroyd (Genesis)
Contact Details: Daniel Cole (daniel.cole@ncl.ac.uk)
PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...] in Newcastle upon Tyne employer: Newcastle University
At Genesis Therapeutics and Newcastle University, we pride ourselves on fostering a collaborative and innovative work environment that empowers PhD students to push the boundaries of drug discovery. With comprehensive funding for home fees and a generous living allowance, alongside opportunities for hands-on experience in cutting-edge research, our students benefit from a supportive culture that prioritises personal and professional growth. Located in the vibrant city of Newcastle, you will have access to a wealth of resources and a dynamic community dedicated to advancing scientific knowledge.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...] in Newcastle upon Tyne
✨Tip Number 1
Network like a pro! Reach out to current PhD students or alumni from the programme. They can give you insider info on what the supervisors are looking for and how to stand out.
✨Tip Number 2
Prepare for your interview by brushing up on both machine learning and computational physics concepts. We want to see that you’re not just passionate but also knowledgeable about the field!
✨Tip Number 3
Showcase your projects! If you've worked on relevant research or have experience with simulations, make sure to discuss these during your interview. It’s all about demonstrating your skills in action.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...] in Newcastle upon Tyne
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the project. We want to see your passion for integrating computational physics and machine learning, so don’t hold back!
Showcase Relevant Experience:Include any relevant projects or research you've done that relates to drug discovery or simulation. We love seeing how you’ve applied your knowledge in real-world scenarios, so share those details with us!
Be Clear and Concise:Keep your writing clear and to the point. We appreciate well-structured applications that are easy to read. Avoid jargon unless it’s necessary, and make sure your enthusiasm shines through!
Apply Through Our Website:Don’t forget to submit your application through our official website. It’s the best way to ensure we receive all your materials and can process your application smoothly. We’re excited to hear from you!
How to prepare for a job interview at Newcastle University
✨Know Your Stuff
Make sure you brush up on both computational physics and machine learning concepts. Be ready to discuss how these fields intersect, especially in drug discovery. Familiarise yourself with recent advancements and be prepared to share your thoughts on how they could apply to the project.
✨Showcase Your Experience
Prepare to talk about any relevant projects or research you've done, particularly those involving simulations or modelling. Highlight specific challenges you faced and how you overcame them. This will demonstrate your problem-solving skills and your ability to contribute to the team.
✨Ask Insightful Questions
Come prepared with questions that show your genuine interest in the role and the research. Inquire about the integration of workflows into drug discovery pipelines or the collaboration with industry partners. This not only shows your enthusiasm but also helps you gauge if the position is the right fit for you.
✨Be Yourself
While it's important to be professional, don't forget to let your personality shine through. The interviewers want to see if you'll fit into their team culture. Share your passion for the subject and your motivation for pursuing this studentship—it can make a lasting impression!
