Mohib Scholarships Network’s Post

AZ have released their latest graduate programme roles in Bioscience, Chemisty and Data Science/AI. These are 2 year programmes based in Cambridge UK to accelerate hands on learning in the relevant field. If you are interested or know someone who might be, have a look at the 3 adverts below Bioscience role - https://lnkd.in/eXWYD_EV Data Science/AI - https://lnkd.in/eBsEsd74 Chemistry - https://lnkd.in/e8fya5bU

💻 Exploring Careers in Life Sciences: Navigating Your Path with a Biology Degree - 🌟 Technology Integration Merge your passion for biology with technology in roles like Data Scientist, Software Engineer, and Biomedical Engineer, where salaries can reach up to £82,500. Curious about how biology and tech combine to create innovative solutions? Contact Circle Life Sciences to navigate these exciting tech-driven career opportunities! 📞 01733 798 302 📧 info@circlelifescience.io #TechInBiology #EngineeringFutures #BiotechCareers

I had the privilege of speaking with a group of MIT students a few weeks ago about what my job entails and pathways to computational biology. I was surprised to hear from them that MIT, which sits literally in Kendall Square, one of the densest biotech/pharma spaces in the world, offers zero career counseling to its computational biology students on how to break into biotech/pharma. These students have few resources to turn to besides LinkedIn randos like me. It’s a shame. So here is a peculiar situation. Here we have some of the brightest young people in the world who want to tackle the hardest compbio problems in biotech/pharma. They receive no guidance on how to navigate this path. I’ll add that many of them have also soured on the idea of a PhD due to its reputation for exploiting students. So doing a PhD for 5-6 years in order to land an entry-level industry job isn’t a practical recommendation for them. And TBH these students don’t need a PhD do to great science; they are actually that good. They just need a way to break into biotech/pharma after college. These MIT students are locked out at the gate. We are basicay telling them to go away. Do something else with their lives other than solve problems that can impact patient outcomes. Go work for Amazon. How did we end up here? What can we do to help them? I am asking especially those who are currently working as computational biologists in biotech/pharma. What do you think? #compbio #computationalbiology #bioinformatics #biotech #pharma

Taking a Leap of Faith Toward My Dreams! After graduating with a Bachelor’s in Neuroscience, I was on the path to fulfilling my father’s wish to become a medical doctor. But deep down, I knew my heart was somewhere else—in computer science and, more specifically, data science. I’ve always been drawn to how data can tell a story, how it can drive real impact, and how certain ML modeling applications can help solve numerous problems in numerous fields (medical research, fintech, robotics, etc.…) So, I decided to take a risk and move to the UK to pursue an MSc in Data Science at the University of Greenwich. And let me tell you, it’s been anything but easy. This isn’t a conversion course; it’s a full dive into technical depths, and I chose it because I wanted to push myself and be surrounded by people who know their stuff. There have been plenty of sleepless nights, tackling the basics and trying to keep pace with classmates. But even on the hard days, I’m grateful. This journey has taught me so much about resilience, commitment, and the power of following what excites you. If you’re thinking about pursuing a new career, personal goal, or academic dream, I just want to say Go for it. Jump, even if it feels scary. And for those already in the field—Data Scientists and Machine Learning Engineers—as those are the two careers that have drawn my attention since starting this journey, I’d love any advice you can share. How can I stand out and become competitive in this industry? Any insights would mean the world to me. #CareerChange #DataScience #TakeTheLeap #Jump #DataScientist #MachineLearningEngineer #UniversityofGreenwich

In my earlier post I listed some of the many ways a senior leader can accelerate and magnify the impact of a comp sci function within a modern early stage biotech. However, I omitted probably the most important role - advocating for the needs of comp sci across the organization. Beyond the obvious (budgets, etc.) there is a much deeper aspect - advocating for how experiments can be done in a way that enables broader analysis and learning. There's a huge difference between having run a lot of assays and having data that's fit for purpose for ML/AI applications - and most organizations don't have an understanding of why there is a difference or how to overcome it embedded within the C-suite. It likely requires significant changes in how science is executed, from involvement of comp sci in the experimental planning stages up to and including changes in organizational structure (e.g. a screening group with embedded comp sci representation). An advisor or fractional CD(S)O/CAIO can help plan and execute a transition strategy to get a place where you can realistically leverage the data being generated for future learning. Get in touch if you'd like to explore how that could work for you!

Recognizing the growing importance of data science in early-stage drug discovery, SGC is launching a new initiative aimed at training the next generation of computational and data scientists. Supported by a grant from Data Sciences Institute, University of Toronto, a program led by Matthieu Schapira will provide trainees with the skills necessary to interpret complex experimental data and drive breakthroughs in drug discovery. The first 9-week bootcamp open to students, postdoctoral researchers and staff with computer or biological science backgrounds will take place from February to April 2025. Interested individuals are encouraged to submit their applications by January 12, 2025, in order to secure their spot with complimentary registration. Learn more: https://lnkd.in/g2iJ774C #SGCNews #DrugDiscovery #TraineeOpportunity #Bootcamp #ComputationalChemistry

If you are applying for a PhD position, please do *not* tell me that you are "results-driven" or "self-driven" with a "proven track record" and "strong foundations" in xyz and a "passion for [complex] problem solving" or "teamwork". I have just read this in dozens of other letters, all polished by AI. Tell me why you are different, and why you want this job. In the absence of useful letters, I find myself looking much more at grades again.

The notion that a "perfect" career in life sciences follows the rigid path of B.Sc. - M.Sc. - Ph.D. - Postdoc - Academic Professor is not just outdated – it's limiting your potential. As life science professionals, we possess a unique set of skills that extend far beyond the laboratory. Our analytical thinking, problem-solving abilities, and deep understanding of complex systems are invaluable across numerous industries. Consider this: You can drive innovation in biotech without running experiments daily. You can shape science policy without writing academic papers. You can lead product development in pharma without being tied to a bench. Are you truly leaving science behind by choosing these paths? Absolutely not. You're still: Interpreting scientific data, just in different contexts Communicating complex ideas, just to varied audiences Solving critical problems, just on a broader scale It's time to recognize that your worth isn't defined by your proximity to a pipette. Your value lies in your ability to apply scientific thinking to real-world challenges. Embrace the multitude of career options available to you. Your life science background is not a constraint – it's a launchpad for a fulfilling career aligned with your personal aspirations. Remember: You're not abandoning science by exploring diverse career paths. You're expanding its reach and impact. ... #LifeScienceCareers #careers #gethired #sciencejobs #beyondacademia #buildyourcareer

I’m really grateful to have experienced doctoral training. As I progress in my career, the soft skills I developed during my PhD, prior to me quitting, have been integral to the successes I’ve achieved in the industry roles I’ve held. Yes, having a PhD means you’ve earned an esteemed credential and, if you’re lucky, a few first-author papers to your name but, I’m so fortunate to have learned things like: 1. Critical thinking • Perhaps the most important skill I’ve carried with me beyond the academy: really framing a problem from hypothesis generation, through method selection and data interpretation to eventual completion. Or measured in industry KPIs, “to eventual tangible business value add.” 2. Reading primary literature • I used to consider this so onerous throughout undergrad and even at the start of grad school. I felt like I didn’t need to read papers. But dissecting methods and inferring conclusions is a skillset I’ve leaned on almost daily to stay abreast with Computational Biology & AI innovations. 3. Learning deeply, quickly • Being forced to collaborate with folks in otherwise unrelated fields meant I needed to become an expert in a particular research area unlike my own, rapidly. This has made me extremely adaptable over the years. 4. Functioning autonomously • No one holds your hand during a PhD. You’ve got to fumble around in the dark for a bit before you [hopefully] find your footing. You are responsible for spearheading your thesis work to completion. Though daunting, it’s made me confident in my own capabilities, even in uncharted territory. Dean Lee talks a lot about this stuff in pursuit of making Comp Bio careers accessible through his Figure One Lab initiative. He inspired this recollection of mine. Check him out. _________ #compbio #research #data #careers #phd

Today,early stage biotech companies typically rely heavily on computational biology, computational chemistry, and/or (especially) ML/AI to progress their platform and/or programs. Yet many of those companies don't have executive leadership in the computational science space (CDO, CDSO, CAIO, or similar) to help set vision and strategy, oversee execution and recruitment, and mentor and develop the (often junior) scientists doing this critical work. That can limit impact and slow progress, losing money and (critically) time. This limitation might not be obvious, but it’s there! If that describes your situation, perhaps a fractional or advisory role with an experienced comp sci leader could help accelerate your progress at a fraction (pun intended) of the cost of a full time C-level role? Please reach out if you'd like to explore this option.