Imran Khan’s Post

*ATTENTION TO ALL MY FRIENDS IN THE TECH UNIVERSE* We just had 8 AMAZING opportunities open up that you really should check out. If you've been thinking about taking your tech superpowers over to science, then THIS is your opportunity to be at the epicenter of the paradigm shift in science and medicine...literally. On top of these being amazing opportunities where you can truly make your mark on an entire industry, Ryan Copping is an outstanding leader and visionary. BIOLOGY IS WHERE IT'S AT!!! Link to the 8 postings (4 Function Heads, and 4 Domain Head/Matrix Team Leader positions): https://lnkd.in/drhBkcvq [note: pls search for #gCSCatalysts and all 8 postings should display] #softwareengineering #softwaredevelopment #dataengineering #datascience #bigdata #artificialintelligence #machinelearning #weareroche

Computer vision in oncology is crucial for developing advanced medical imaging models that help in the non-invasive analysis of cancer, enabling precise diagnosis, monitoring, and treatment planning. Computer vision contributes significantly to personalized medicine and improved patient outcomes by analyzing imaging biomarkers. #oncology #datascience #radiomics #computervision

The time spent on documenting your experiments obviously depends on how many experiments you run, what type of experiments, etc. I used to spend about 30% of my time in the lab documenting my experiments. I used to run a lot of experiments.... Then I created a number of "Templates" and brought more structure to my research documentation and managed to cut that down by about 10%. I think with IGOR, thanks to the way it automates and streamlines most of the documentation tasks, I would probably be in the 10-15% range now. IGOR makes it so easy to create Templates and SOPs and share them with your team, scan hand-written notes and upload them straight to your lab notebook entries, upload data files, etc. Consider the remarkable acceleration in scientific progress that could be achieved if the time your scientists dedicate to research documentation were reduced from 25% to just 15%... ✨ 🔬

We are #hiring at Evotec! 💥 👨💻 Are you passionate about Omics data? Are you excited about coding? Do you want to work in a global and collaborative team? If so, come and join our #team! 👩💻 Omics data play a key role in drug discovery at Evotec today. Our goal is to create an omics data continuum from the initial disease phenotype all the way to patient stratification for precision medicine in the clinic. For more information please click on the link below ⬇ https://lnkd.in/e2svWJwf or reach out to me via DM should you have any further questions. #evotec #researchneverstops #BeCUREious #bioinformatics #science #global #career #goettingen

Suppose I meet 80% of a job’s requirements. Which of the other 20% are hard requirements? Which ones are teachable? This was a question I got from a client the other day. Here's my perspective: In reality, everything is teachable. The challenge is time, and that is something that very few biotech companies have. Here are some considerations that can guide you. The first thing to consider is whether the requirements have to do with regulatory experience. In general, if there’s a requirement for previous experience filing INDs or working with the FDA, that’s non-negotiable. But what about technical skills? Here it takes a bit more nuance. Ask yourself, do you have experience working with adjacent techniques, and how established is the company? 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟭 If you’ve done something adjacent to the requirement, you can argue that learning a similar technique can be done independently and quickly. For example, if you’ve done bulk RNA sequencing, learning single cell sequencing might not be a big challenge for you compared to someone who has never done sequencing at all. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟮 If the company is well-established and specialized, it’s less important to already know the skills. For example, if you’re joining a flow cytometry based company that has been operating for 20+ years, it’s not critical that you’re an expert in flow cytometry. You should have some familiarity with the technique and know that the company has training programs to teach you the rest quickly. On the other hand, if you’re joining a 10 person startup and nobody there has flow experience, they need you to come in as an expert that can build the systems and teach others how to use the systems as well. 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀? -Regulatory is usually non-negotiable -Adjacent skills are better than unrelated skills -Large companies are more willing to train you Use this information to help you decide which jobs could be a good fit FOR YOU. #biotech, #biotechjobs, #scientist

The AI/ML Sequence Learning Team at GSK applies #machinelearning and #AI methods to biological sequence (DNA, RNA, protein) data from large-scale #humangenetics, #functionalgenomics, and #singlecell experiments. Models operating directly on sequence data that can infer how variants alter protein/RNA abundance, structure, and function have the potential to be transformative in #drugdiscovery, empowering the development of new life-saving medicines. The team is looking to hire an experienced #ML Engineer experienced with biological data (e.g., #genomics, #transcriptomics, #epigenomics, #proteomics) and shipping stable, tested, performant code in an agile environment to bring this vision into reality. Please apply directly through the posting below! #pharma #pharmaceutical #pharmaceuticals #pharmajobs #rnastructure #rnafunction #drugdevelopment #mlengineer #mlengineering #softwareengineer #softwareengineering #healthcare #python #careeropportunities #computerscience #appliedmath #proteinsequencing #likeandshare https://lnkd.in/e8-QaSpr

Are you part of the 54.5% club? In this club 54.5% of respondents shared that their organizations aim to reduce ML model deployment time by at least 25% over the next 12 months. If you know your WHY and WHAT and are still missing the "HOWs" like: 1. How to shorten model deployment times from weeks to one day (or hours) 2. How to avoid hiring more developers to maintain your infrastructure and deployments? You need to check out the story of the Lifebit team doing exactly that with Valohai: https://lnkd.in/ddgGmevP “We used to have an engineer or two allocated to support deploying models. With Valohai, anyone on the team can deploy independently. The reduction in overhead is immense.” - says Rishabh Shukla, Lead ML engineer at Lifebit. Lifebit is a genomics and bioinformatics company that uses #MachineLearning to help data custodians make their data findable and usable for data consumers.