Hybrid Conference (In Person / Virtual)

International Conference on Stochastic Modeling in Environmental and Biological Systems (ICSMEBS - 27)
23rd - 24th January 2027 , Stockholm, Sweden
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Call For Papers

The ICSMEBS provides a supportive platform for both experienced researchers and early-career academicians to present their work and gain recognition.

The conference covers diverse topics such as Probability Theory, Statistics, encouraging participation from emerging researchers and fostering academic growth.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Stochastic modeling in ecological systems
02
Applications of stochastic modeling in biology
03
Stochastic processes in environmental risk assessment
04
Modeling biological systems with stochastic methods
05
Stochastic modeling in climate change studies
06
Applications of stochastic modeling in agriculture
07
Stochastic processes in wildlife management
08
Stochastic modeling for public health
09
Applications of stochastic modeling in conservation
10
Stochastic modeling in epidemiological studies
11
Stochastic processes in environmental policy
12
Modeling environmental systems with stochastic methods
13
Stochastic modeling in water resource management
14
Applications of stochastic modeling in forestry
15
Stochastic processes in urban ecology
16
Stochastic modeling in fisheries management
17
Stochastic approaches to biodiversity assessment
18
Applications of stochastic modeling in pollution control
19
Stochastic modeling in renewable energy systems
20
Stochastic processes in land use planning

Assessment

Submissions will be reviewed to ensure quality and relevance, with a focus on encouraging emerging research contributions. Accepted papers will be presented and considered for publication opportunities.

Registration

Early-career researchers are encouraged to register and present their work, gaining valuable feedback and academic exposure.

Publication

The conference provides opportunities for emerging researchers to publish their work in recognized platforms.