Intern: Deep Learning

Bayer
Bayer

Job Overview

**Position Overview:**The Climate Corporation’s mission is, “to help all the world’s farmers sustainably increase their productivity with digital tools.” Part of this entails building models to understand and optimize crop growth. The inherent complexity of the process and the limited size and availability of agriculturally relevant datasets has hampered progress in fully understanding crop growth to date. However, as an industry leader and with access to unique and increasingly rich agricultural datasets, The Climate Corporation is positioned to make significant advances in understanding this problem.In addition to its complexity, there are a number of practical challenges to modeling crop growth. These include the heterogeneity of the data in terms of its feature modality and sampling frequency, lack of explanatory features, and unpredictable quality of the data. Multi-task learning has recently gained popularity in the field of deep learning to help tackle the challenges of similarly complicated and poorly constrained real-world problems including self-driving vehicles, natural language processing and numerous medical applications. As a deep learning intern you will explore the application of multi-task learning to modeling crop growth.**What You Will Do:**+ Develop novel models to solve an increasingly relevant global problem using unprecedented agricultural datasets+ Test and develop your own creative solutions with ample computational resources+ Collaborate with domain experts from a variety of backgrounds (e.g. breeding, agronomy, plant pathology, engineering, statistics, etc.)+ Positively impact farmers and business applications across our suite of products**Basic Qualifications**+ Currently enrolled in a graduate program in Computer Science, Computer Engineering, Statistics, Mathematics, or a related discipline with a focus on deep learning+ Proficient in python and at least one deep learning framework (tensorflow, JAX, pytorch, etc.)+ Experience implementing custom deep learning models+ Demonstrable ability to apply concepts from current deep learning publications**Preferred Qualifications**+ Familiarity with multi-task learning, meta learning, or related sub fields+ Current PhD candidate+ Experience publishing research+ Experience developing models on spatial and/or temporal datasets (e.g. using RNNs, CNNs, or attention mechanisms)+ Experience developing models using heterogeneous or multi-modal datasets+ Strong analytical, written, and verbal communication skills**What We Offer:**Our environment is extremely engaging and fast-paced, with a diverse set of top engineers, agronomists, and statisticians working together to provide the best possible products and experiences for our customers.+ We offer competitive pay and perks.+ You’ll get 1:1 mentoring from a talented and motivated employee on your team.+ You’ll have the opportunity to work on real projects with experienced employees and have a direct impact on the company. (Our internships are not summer camp!)+ We regularly host meet-up groups and tech-talks and encourage participation in relevant workshops and conferences+ You’ll have the opportunity to interact with key executives and leaders.Learn more about our team and our mission:The Climate Corporation – The Technology Behind Making A Differencehttps://youtu.be/c5TgbpE9UBIClimate aims to create a welcoming and collaborative environment for our employees in which a diverse set of perspectives and voices are represented and celebrated.As part of our dedication to the diversity of our workforce, The Climate Corporation is committed to Equal Employment Opportunity and does not discriminate based on race, religion, color, national origin, ethnicity, gender, sex (including pregnancy), protected veteran status, age, disability, sexual orientation, gender identity, gender expression, or any unlawful criterion existing under applicable federal, state, or local laws. If you need assistance or an accommodation due to a disability, you may contact us at [email protected] .

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