Freshers PhD Jobs at Growdea Technology
Freshers PhD Jobs at Growdea Technology. Job Opening For In-Silico Biologist Position at Growdea Technology – Freshers Can Apply. Interested and eligible applicants can check out all of the details on the same below
Are you ready to make a meaningful impact and take your career to new heights? We’re excited to announce a job opening for the position of In-Silico Biologist at Growdea Technology.
As an In-Silico Biologist with us, you’ll have the chance you will play a pivotal role in harnessing computational tools to drive innovation and accelerate our research efforts. We’re seeking a dynamic and motivated individual who is ready to work with startup and become an active member of our success journey.
Key Responsibilities:
Biological Network Analysis: Construct and analyze complex biological networks to unravel intricate relationships between genes, proteins, and pathways, offering novel perspectives for target identification and therapeutic development.
Data Analysis and Processing: Perform bioinformatics analysis on NGS data, including raw sequencing reads, to preprocess, align, and quality-filter data for downstream analysis.
Variant Calling and Annotation: Utilize established pipelines and software tools to accurately call and annotate genetic variants, including single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs).
Genomic Data Interpretation
: Analyze and interpret variant data in the context of known genetic databases, functional annotations, and relevant literature to determine potential functional impacts.Structural Variant Analysis: Identify and characterize structural variants, such as copy number variations (CNVs) and translocations, using computational approaches.
Virtual Screening and Drug Discovery: Apply in-silico screening methods to identify potential drug candidates, evaluate their binding affinity, and contribute to the discovery of novel therapeutic agents.
Additional Responsibilities:
Machine Learning Integration: Collaborate with data scientists to integrate machine learning and AI techniques, enhancing predictive models and uncovering hidden patterns in biological data.
Qualifications:
- PhD in Computational Biology, Bioinformatics.
- Fresh PhDs can also apply.
Job Location:
Plot-76-D, Ist Floor, Phase IV
Gurugram, Haryana – 122001
Growdea offers a collaborative and innovative work environment, competitive compensation package, and opportunities for professional growth.
How to Apply:
Please fill the google form: https://lnkd.in/d6Bwd6sZ
Last Date: 15th September 2023
Here are five possible interview questions that can be asked in the technical round for the In-Silico Biologist position, along with their answers:
1. Question: Can you explain a specific project where you applied biological network analysis to uncover insights into gene-protein-pathway relationships? What tools or methods did you use, and what were the outcomes? Answer: Certainly. In a recent project, I constructed a protein-protein interaction network using various databases and performed network analysis to identify key hub genes. This helped uncover potential regulatory mechanisms and target candidates for therapeutic intervention. I utilized tools like Cytoscape to visualize the network and identify essential pathways associated with the hub genes.
2. Question: Describe your experience with variant calling and annotation from NGS data. How do you ensure accuracy in identifying genetic variants? Answer: In my previous role, I employed established pipelines like GATK for variant calling and utilized public databases such as dbSNP and ClinVar for variant annotation. To ensure accuracy, I followed best practices for data preprocessing, quality filtering, and recalibration. Additionally, I considered read depth, mapping quality, and variant frequency to filter out false positives and retain genuine variants.
3. Question: Could you share an example of a structural variant analysis you’ve conducted using computational approaches? What insights did you gain from this analysis? Answer: Certainly. In a recent study, I utilized tools like DELLY and BreakDancer to detect copy number variations (CNVs) in cancer genomes. This analysis revealed significant genomic alterations associated with tumor progression. I further correlated CNVs with gene expression data to identify potential oncogenes and tumor suppressors linked to the observed structural variants.
4. Question: How have you applied in-silico screening methods for virtual drug discovery? Can you elaborate on a successful instance where you identified potential drug candidates using computational techniques? Answer: In a project focused on antiviral drug discovery, I employed molecular docking and molecular dynamics simulations to screen a library of compounds against a viral protein target. By evaluating binding affinities and conducting interaction analyses, I identified several compounds with promising binding profiles. These findings contributed to selecting lead candidates for further experimental validation.
5. Question: Collaboration between computational biologists and data scientists is crucial for integrating machine learning techniques. Can you share an example of how you’ve collaborated to enhance predictive models in biological research? Answer: In a collaborative effort, I worked with data scientists to develop a machine learning model for predicting disease-associated genetic variants. I provided input on feature selection, domain-specific insights, and biological relevance of features. By combining my domain knowledge with their expertise in machine learning, we optimized the model’s performance and identified potential novel markers associated with the disease.
These questions aim to assess your hands-on experience in applying computational methods, your problem-solving skills, and your ability to collaborate with interdisciplinary teams for innovative research in bioinformatics and computational biology.
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