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DSTC-00477 Online (e-LMS) Graduate / Intermediate

The Battery Genome Project: AI for Energy Storage

by - DSTC

Accelerate battery discovery with AI and materials data.

โ˜…โ˜…โ˜…โ˜…โ˜… Be the first to review โ€ข 4 Weeks ยท 40 hrs โ€ข e-Certificate Included
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From โ‚น2,500 + GST

๐Ÿ’ป Software & Environment Setup

Bioinformatics & Computational Biology

The computational toolchain and environment used throughout The Battery Genome Project: AI for Energy Storage.

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# Recommended conda environment
conda create -n dstc_env python=3.11 -y
conda activate dstc_env
pip install biopython pandas pysam scikit-bio
Biopython
Sequence I/O, BLAST parsing, phylogenetics
SAMtools / BCFtools
Alignment & variant file manipulation
Bioconductor (R)
DESeq2, edgeR & limma for expression analysis
Nextflow
Reproducible, portable NGS pipelines
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.

Earn government-registered certification in The Battery Genome Project: AI for Energy Storage

e-Certificate and e-Marksheet issued on successful completion.

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Scholar Registration

For scholars whose department, college or employer pays the fee. We raise a proforma invoice to your institution; you attach the signed processing letter or bank slip.

The proforma invoice is emailed here as well as to you.
๐Ÿ“„ Upload Sponsorship Slip / Letter

Signed letter on official letterhead, or the bank transfer slip. PDF/JPG/PNG, up to 5 MB.

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