Lab Digital Solution Developer – Contract

Job not on LinkedIn

November 3

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Logo of Catalyst Education

Catalyst Education

Education • SaaS • Science

Catalyst Education is a company focused on enhancing educational experiences by providing innovative digital teaching and learning software specifically designed for laboratory courses in chemistry and biology. Their platform, Labflow, facilitates hands-on experimental learning with pre-lab preparations, post-lab coaching, and effective course management tools that support educators in various settings, including in-person, hybrid, and online formats. Catalyst Education aims to streamline course administration while improving learning outcomes for both students and instructors across numerous educational institutions.

11 - 50 employees

Founded 2018

📚 Education

☁️ SaaS

🔬 Science

💰 Non Equity Assistance on 2019-09

📋 Description

• Translating traditional experimental procedures and report sheets into Labflow’s proprietary digital format (DataFlow Module) • Selecting and embedding elements (e.g., prompts, tables, dropdowns, essay fields) into custom DataFlow Modules to optimize a given experiment for the digital format • Translating constraints of laboratory equipment like precision and measurement range into data entry and procedure constraints in digital reports • Writing JavaScript autograding procedures to transform student-entered data into formulas that automatically assess responses in the results and analysis sections of DataFlow Modules • Writing & debugging HTML and LaTeX markup, JSON notation

🎯 Requirements

• An individual with experience in laboratory chemistry or biology and some knowledge of web languages. • A qualified candidate must have experience in areas of undergraduate laboratory instruction and should have some experience with HTML and/or LaTeX. • The ideal candidate will have pedagogical experience within undergraduate laboratories, have good knowledge of HTML, LaTeX, JavaScript scripting and JSON notation, and be comfortable translating traditional experimental procedures into discrete components that can be automatically graded. • Candidates should be highly motivated and exhibit excellent organizational skills.

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